# C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient
Source Free Domain Adaptation

Nazmul Karim∗, Niluthpol Chowdhury Mithun†, Abhinav Rajvanshi†, Han-pang Chiu†
Supun Samarasekera†, Nazanin Rahnavard∗ ∗Department of ECE, UCF, Orlando FL
USA  †SRI International, Princeton, NJ, USA<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Unsupervised domain adaptation (UDA) approaches focus on adapting models
trained on a labeled source domain to an unlabeled target domain. In contrast
to UDA, source-free domain adaptation (SFDA) is a more practical setup as
access to source data is no longer required during adaptation. Recent state-
of-the-art (SOTA) methods on SFDA mostly focus on pseudo-label refinement
based self-training which generally suffers from two issues: i) inevitable
occurrence of noisy pseudo-labels that could lead to early training time
memorization, ii) refinement process requires maintaining a memory bank which
creates a significant burden in resource constraint scenarios. To address
these concerns, we propose C-SFDA, a curriculum learning aided self-training
framework for SFDA that adapts efficiently and reliably to changes across
domains based on selective pseudo-labeling. Specifically, we employ a
curriculum learning scheme to promote learning from a restricted amount of
pseudo labels selected based on their reliabilities. This simple yet effective
step successfully prevents label noise propagation during different stages of
adaptation and eliminates the need for costly memory-bank based label
refinement. Our extensive experimental evaluations on both image recognition
and semantic segmentation tasks confirm the effectiveness of our method.
C-SFDA is also applicable to online test-time domain adaptation and
outperforms previous SOTA methods in this task.

11footnotetext: Most of this work was done during Nazmul Karim’s internship
with SRI International. Project Page:
https://sites.google.com/view/csfdacvpr2023/home

##  1 Introduction

Deep neural network (DNN) models have achieved remarkable success in various
visual recognition tasks [41, 22, 16, 44]. However, even very large DNN models
often suffer significant performance degradation when there is a distribution
or domain shift [78, 54] between training (source) and test (target) domains.
To address the problem of domain shifts, various Unsupervised Domain
Adaptation (UDA) [19, 31] algorithms have been developed over recent years.
Most UDA techniques require access to labeled source domain data during
adaptation, which limits their application in many real-world scenarios,
_e.g_. source data is private, or adaptation in edge devices with limited
computational capacity. In this regard, source-free domain adaptation setting
has recently gained significant interest [34, 35, 84], which considers the
availability of only source pre-trained model and unlabeled target domain
data.

Recent state-of-the-art SFDA methods (e.g., SHOT [42], NRC [83], G-SFDA [85],
AdaContrast [4]) mostly rely on the self-training mechanism that is guided by
the source pre-trained model generated pseudo-labels (PLs). Label refinement
using the knowledge of per-class cluster structure in feature space is
recurrently used in these methods. At early stages of adaptation, the label
information formulated based on cluster structure can be severely misleading
or noisy; shown in Fig. 1. As the adaptation progresses, this label noise can
negatively impact the subsequent cluster structure as the key to learning
meaningful clusters hinges on the quality of pseudo-labels itself. Therefore,
the inevitable presence of label noise at early training time is a critical
issue in SFDA and requires proper attention. Furthermore, distributing cluster
knowledge among neighbor samples requires a memory bank [4, 42] which creates
a significant burden in resource-constraint scenarios. In addition, most
memory bank dependent SFDA techniques are not suitable for online test-time
domain adaptation [73, 76]; an emerging area of UDA that has gained traction
in recent times. _Designing a memory-bank-free SFDA approach that can guide
the self-training with highly precise pseudo-labels is a very challenging task
and a major focus of this work._

Figure 1: _Left:_ In SFDA, we only have a source model that needs to be
adapted to the target data. Among the source-generated pseudo-labels, a large
portion is noisy which is important to avoid during supervised self-training
(SST) with regular cross-entropy loss. Instead of using all pseudo-labels, we
choose the most reliable ones and effectively propagate high-quality label
information to unreliable samples. As the training progresses, the proposed
selection strategy tends to choose more samples for SST due to the improved
average reliability of pseudo-labels. Such a restricted self-training strategy
creates a model with better discrimination ability and eventually corrects the
noisy predictions. Here, $T$ is the total number of iterations. _Right:_ While
existing SFDA techniques leverages cluster structure knowledge in the feature
space, there may exist many misleading neighbors— pseudo-labels of neighbors’
that are different from the anchors’ true label. Therefore, clustering-based
label propagation inevitably suffers from label noise in subsequent training.

In our work, we focus on increasing the reliability of generated pseudo-labels
without using a memory-bank and clustering-based pseudo-label refinement. Our
analysis shows that avoiding early training-time memorization (ETM) of noisy
labels encourages noise-free learning in subsequent stages of adaptation. We
further analyze that even with an expensive label refinement technique in
place, learning equally from all labels eventually leads to label-noise
memorization. Therefore, we employ a curriculum learning-aided self-training
framework, C-SFDA, that prioritizes learning from easy-to-learn samples first
and hard samples later on. We show that one can effectively identify the group
of easy samples by utilizing the reliability of pseudo-labels, _i.e_.
prediction confidence and uncertainty. We then follow a carefully designed
curriculum learning pipeline to learn from highly reliable (easy) samples
first and gradually propagate more refined label information among less
reliable (hard) samples later on. In addition to the self-training, we
facilitate unsupervised contrastive representation learning that helps us
prevent the early training-time memorization phenomenon. Our main
contributions are summarized as follows:

  * •

We introduce a novel SFDA technique that focuses on noise-free self-training
exploiting the reliability of generated pseudo-labels. With the help of
curriculum learning, we aim to prevent early training time memorization of
noisy pseudo-labels and improve the quality of subsequent self-training as
shown in Fig. 1.

  * •

By prioritizing the learning from highly reliable pseudo-labels first, we aim
to propagate _refined and accurate label information_ among less reliable
samples. Such a selective self-training strategy eliminates the requirement of
a computationally costly and memory-bank dependent label refinement framework.

  * •

C-SFDA achieves state-of-the-art performance on major benchmarks for image
recognition and semantic segmentation. Being highly memory-efficient, the
proposed method is also applicable to online test-time adaptation settings and
obtains SOTA performance.

##  2 Related Work

UDA: UDA for visual recognition tasks has been widely studied in the
literature [10, 74]. Adversarial learning [23, 70, 72, 47], image-to-image
translation [51, 36, 23], cross-domain divergence minimization [39, 3, 67,
64],and optimal transport [12, 5, 81] are popular techniques across prior
works on UDA. Self-training [80, 17, 92, 49, 86] has recently been a dominant
trend in UDA, which uses labeled source data and pseudo-labeled target data
(typically generated using a teacher model) to iteratively train a student
model. Different variants of self-training such as cycle self-training [43],
PL Curriculum Learning [8], Selective pseudo-labelling [75] have been proposed
to utilize pseudo-labeling in effective manners. Although previous works
employed selective pseudo-labeling and curriculum learning in UDA, we aim to
exploit these two mechanisms under a new and more challenging scenario (SFDA).
This requires us to devise a unique framework for curriculum learning, the
effectiveness of which depends on our proposed selective pseudo-labeling
technique.

SFDA: In recent years, several approaches [42, 37, 46, 79, 85, 34, 69, 82, 56,
4, 14] addressing the source-free domain adaptation problem has been proposed.
SHOT [42] utilizes a centroid-based label refinement technique that guides the
self-training. G-SFDA [85] and NRC [83] follow a similar strategy with further
measures for refining pseudo-labels by encouraging consistent predictions
between local neighbor samples. In addition to label refinement, AdaContrast
[4] leverages MoCo [21] like contrastive feature learning for SFDA by
excluding the same class negative pairs detected by the pseudo-labels.
However, pseudo-labels generated at the early training stage can be noisy, a
fact that has not been well-addressed in these works. Moreover, almost all of
these label refinement strategies require having a large memory queue which is
undesirable in many real-world scenarios. In contrast, our proposed method
reduces the risk of label noise memorization without any rigorous and
expensive pseudo-label refinement technique.

Figure 2: Overview of our proposed method where we use teacher predictions to
train the student model. At the early stage of training, we only consider
reliable pseudo-labels for CE loss as well as contrastive loss for
unsupervised feature representation learning. As the model gets more
confident, we use label propagation loss to distribute high-quality label
information, learned from reliable pseudo-labels, among unreliable samples.

##  3 Proposed Method

We start by defining the source domain dataset,
$\mathcal{D}_{\mathsf{s}}=\\{(x_{\mathsf{s}}^{i},y_{\mathsf{s}}^{i})\\}_{i=1}^{N_{\mathsf{s}}}$
containing $N_{s}$ labeled samples distributed over $K$ number of classes.
Here, $y\in\mathcal{Y}_{\mathsf{s}}\subseteq\mathbb{R}^{K}$ denotes the one-
hot ground-truth label for sample $x$. Let
$f_{\theta_{\mathsf{s}}}:x_{s}\rightarrow y_{s}$ be a source model trained on
$\mathcal{D}_{\mathsf{s}}$. In general, model $f$ consists of a feature
extractor $\mathbf{G}$ and a fully connected (FC)-layer based classifier
$\mathbf{C}$. We denote the target domain dataset,
$\mathcal{D}_{\mathsf{t}}=\\{(x_{\mathsf{t}}^{i})\\}_{i=1}^{N_{\mathsf{t}}}$
containing $N_{\mathsf{t}}$ unlabeled samples. We have access to the target
labels $\\{y_{\mathsf{t}}^{i})\\}_{i=1}^{N_{\mathsf{t}}}$ for evaluation only.
$\mathcal{D}_{\mathsf{s}}$ and $\mathcal{D}_{\mathsf{t}}$ have same underlying
label distribution with a common label set $\mathcal{C}=\\{1,2,\cdots,K\\}$.
In this paper, we focus on SFDA problem where access to
$\mathcal{D}_{\mathsf{s}}$ is unavailable while adapting
$f_{\theta_{\mathsf{s}}}$ on $\mathcal{D}_{\mathsf{t}}$.

###  3.1 Curriculum SFDA

We employ a pseudo-labeling based self-training mechanism for SFDA. Consider a
self-training framework shown in Fig. 2 where we use a teacher model
($f_{\hat{\theta}_{\mathsf{t}}}$) for generating the target pseudo-label for a
student model ($f_{\theta_{\mathsf{t}}}$). At the start of training, both
models share the same weights, _i.e_.
$\theta_{\mathsf{t}}=\hat{\theta}_{\mathsf{t}}=\theta_{\mathsf{s}}$. For
generating the target pseudo-label, we simply use the teacher model
prediction, $\hat{y}_{\mathsf{t}}$. Considering a batch size of $B$, we use
the batch of pseudo-labels
$\mathcal{Y}_{\mathsf{t}}=\\{\hat{y}_{\mathsf{t}}^{(i)}\\}_{i=1}^{B}$ to
update $\theta_{\mathsf{t}}$ using a cross-entropy (CE) loss,

$\vspace{-0.01cm}\mathcal{L}_{ce}=-\frac{1}{B}\sum_{i=1}^{B}{\hat{y}_{\mathsf{tc}}^{i}\cdot\log
f_{\theta_{\mathsf{t}}}(x_{\mathsf{t}}^{i}))},\vspace{-1mm}$ (1)

where $\hat{y}_{\mathsf{tc}}^{i}$ is one hot encoding of
$\hat{y}_{\mathsf{t}}^{i}$. Minimizing $\mathcal{L}_{ce}$ enforces consistency
between student and teacher predictions.

In our self-training scenario, there is an inevitable presence of noisy labels
in $\mathcal{Y}_{\mathsf{t}}\in\mathbb{R}^{B}$ which often leads to non-
optimal model performance [1]. As an initial label-noise preventive measure,
we intend to produce stable and high-quality pseudo-labels following these two
simple steps: _i) augmentations averaged prediction, and ii) weight averaged
teacher model_. We execute the first step by taking the augmentation-averaged
teacher predictions,

$\hat{y}_{\mathsf{t}}=\operatorname*{arg\,max}\frac{1}{L}\sum_{l=1}^{l=L}\hat{h}^{l}_{t}=\operatorname*{arg\,max}\frac{1}{L}\sum_{l=1}^{l=L}f_{\hat{\theta}_{\mathsf{t}}}(\hat{x}_{\mathsf{t}}^{l}).$
(2)

Here, $\hat{x}_{\mathsf{t}}^{l}$ is the $l^{\mathsf{th}}$ augmented copy of
the input $x_{\mathsf{t}}$ and $L$ _(=12 in our case)_ is the total number of
augmentations. Data augmentation [11] has been widely used to improve model
generalization performance to unseen data [65, 33]. In our case, however, we
aim to generate consistent teacher model predictions over augmented input
distributions. Although augmentations can be dataset-specific and manually
designed [65, 33, 42], we use a general augmentation policy that is applicable
to multiple datasets. For the second step, we simply take the exponential
moving average (EMA) of student weights to update the teacher model during
each iteration ($j\rightarrow j+1$),

$\hat{\theta}_{t}^{j+1}=\gamma\hat{\theta}_{t}^{j}+(1-\gamma)\theta_{t}^{j+1},$
(3)

where $\gamma$ is a smoothing factor that controls the degree of change we
want during each iteration. However, regardless of these measures, adopting an
entire batch of pseudo-labels for self-training eventually lead to the
memorization of noisy labels. To alleviate this, we propose a curriculum
learning aided self-training strategy that encourages learning from high
reliable pseudo-labels.

####  3.1.1 Selective Pseudo-Labelling

We describe the process of reliable label selection first as it is a vital
part of the proposed curriculum learning strategy. To measure the label
reliability, we intend to exploit two widely-used [15, 57, 77, 25] statistics:
_i) prediction confidence and ii) average entropy or uncertainty_. In general,
if a model is well-calibrated, the accuracy of that model can be strongly
related to prediction confidence [20]. Therefore, prediction confidence can be
a reliable measure of pseudo-label accuracy in SFDA settings. In addition to
the confidence score, [20] shows that difference in entropy can be a reliable
estimate for different types of domain shift. While [20] assumes the
availability of both source and target domains, we are restricted to target
domain data only. Therefore, we use a carefully designed augmentation policy
to create a virtual distribution shift among target domain data. Prediction
variance or uncertainty over augmented distributions should give us a close
estimate of actual domain shift. To this end, we assign a binary reliability
score (${r}^{i}$) to each target sample based on their prediction confidence
and prediction uncertainty ($g_{u}^{i}$),

$\vspace{-2mm}{r}^{i}=\begin{cases}1,&\text{if}\hskip 2.84526pt\textit{
conf}(\hat{h}_{\mathsf{t}}^{i})\geq\tau_{c}\hskip 5.69054pt\text{and}\hskip
5.69054ptg_{u}^{i}\leq\tau_{u}\\\
0,&\text{otherwise}.\end{cases}\vspace{-1mm}$ (4)

We calculate $g_{u}^{i}$ by taking the standard deviation (std.) over
augmentation-based predictions,
$g_{u}^{i}=\textit{std}\\{conf(\hat{h}_{\mathsf{t}}^{l})\\}_{l=1}^{l=L}$. We
particularly consider aleatoric uncertainty [28] here since it better
addresses the concern of domain shift. The pair of selection thresholds
$\tau_{c}$ and $\tau_{u}$ can be estimated as

$\vspace{-1mm}\tau_{c}=\frac{1}{B}\sum_{i=1}^{i=B}\textit{
conf}(\hat{h}_{\mathsf{t}}^{i});\hskip
8.53581pt\tau_{u}=\frac{1}{B}\sum_{i=1}^{i=B}g_{u}^{i}.\vspace{-1mm}$ (5)

Taking the average as a threshold eliminates the requirement of per-dataset
hyper-parameter tuning and makes our selection process highly adaptive. Note
that, the proposed selection strategy is also applicable to fully test-time
adaptation [73, 4].

####  3.1.2 Loss Functions

After getting the reliability score for each sample, we separate the input
batch $\mathbb{D}$ into _more reliable (R)_ and _less reliable (U)_ groups,
$\mathbb{D}_{R}=\\{(x_{\mathsf{t}}^{i},\hat{y}_{\mathsf{t}}^{i}):r_{i}=1\\}_{i=1}^{B}$
and
$\mathbb{D}_{U}=\\{(x_{\mathsf{t}}^{i},\hat{y}_{\mathsf{t}}^{i}):r_{i}=0\\}_{i=1}^{B}$.
While this gives us a good estimate of reliable samples, $\mathbb{D}_{R}$ may
lack diverse samples (sometimes, missing some categories completely). As a
potential remedy to this, we choose a few samples from $\mathbb{D}_{U}$ based
on another metric: _Top-2 confidence score difference (DoC)_ and consider them
as reliable. Finally, we employ class-balanced cross-entropy loss for
$\mathbb{D}_{R}$ ($\mathcal{L}_{ce}^{R}$) with an inverse frequency loss-
weighting factor ($\lambda_{k}$) that accounts for the label imbalance in
$\mathbb{D}_{R}$. Details of DoC and $\lambda_{k}$ are in supplementary. For
$\mathbb{D}_{U}$, we employ label propagation loss[90] as follows,

$\vspace{-1mm}\mathcal{L}_{\mathcal{P}}=\frac{1}{2|\mathbb{D}_{U}|}\sum_{i=1}^{|\mathbb{D}_{U}|}\|f_{\theta_{\mathsf{t}}}(x_{\mathsf{t}}^{i})-\hat{y}_{\mathsf{tc}}^{i}\|^{2}.\vspace{-0.5mm}$
(6)

Due to the transductive property of $\mathcal{L}_{\mathcal{P}}$, it propagates
label information from $\mathbb{D}_{R}$ to $\mathbb{D}_{U}$.

Note that both $\mathcal{L}_{CE}^{R}$ and $\mathcal{L}_{P}$ require pseudo-
label which may lead to memorization; depending on the success in selection
stage. In addition to supervised self-training, learning useful
representations of images in an unsupervised manner may reduce the risk of
memorization. One such approach is _fully unsupervised contrastive learning
(CL)_ where meaningful representation learning becomes possible by enforcing
similarity between two augmented copies of each sample $x_{t}$,
${x_{t}}^{aug,1}$ and ${x_{t}}^{aug,2}$. To this end, we employ a projection
head $\mathbf{H}$ to obtain feature projections
$q_{i}=\mathbf{H}(\mathbf{G}({x_{t}}^{aug,1}))$, and
$q_{j}=\mathbf{H}(\mathbf{G}({x_{t}}^{aug,2}))$ that gives us the contrastive
criterion [7, 32] as

$\displaystyle\vspace{-1mm}\ell_{i,j}$
$\displaystyle=-\log\frac{\exp(\mathrm{sim}(q_{i},q_{j})/\kappa)}{\sum_{b=1}^{2B}1_{b\neq
i}\exp(\mathrm{sim}(q_{i},q_{b})/\kappa)}~{},$ (7)
$\displaystyle\mathcal{L}_{\mathcal{C}}=\frac{1}{2B}\sum_{b=1}^{2B}[\ell_{2b-1,2b}+\ell_{2b,2b-1}],\vspace{-1mm}$
(8)

where $1_{b\neq i}$ is an indicator function that gives a 1 if $b\neq i$,
$\kappa$ is a temperature constant and $\mathrm{sim}(q_{i},q_{j})$ is the
cosine similarity between $q_{i}$ and $q_{j}$. Even though label-dependent
contrastive learning has been employed for SFDA[4], we focus on label-
independent CL to minimize the effect of label noise; especially at an early
stage of training. Finally, the total loss can be expressed as

$\mathcal{L}_{tot}=\mu_{r}\mathcal{L}_{ce}^{R}+(1-\mu_{r})\mathcal{L}_{\mathcal{P}}+\mu_{c}\mathcal{L}_{\mathcal{C}},$
(9)

where $\mu_{r}$ and $\mu_{c}$ are loss coefficients that dictate the pace of
curriculum learning we propose next.

####  3.1.3 Curriculum Learning

Curriculum Learning [2, 91] promotes the strategy of learning from easier
samples first and harder samples later. Our selection strategy in Section
3.1.1 provides us with an estimation of easy and hard groups. Since pseudo-
labels in $\mathbb{D}_{R}$ are most likely to be correct, DNN finds it easier
to learn from them. On the other hand, learning from $\mathbb{D}_{U}$ should
be more restricted due to the presence of a higher noise level. Therefore, we
set an update equation for $\mu_{r}$ as

$\mu_{r}^{j}=\mu_{r}^{j-1}(1-\alpha e^{-\frac{1}{d^{j}}}),$ (10)

where $d^{j}=\frac{\tau_{u}}{\tau_{c}}$ is difficulty score of current batch
of samples and $\mu_{r}^{j-1}$ is the labeled loss coefficient at previous
iteration. We set $\alpha$ and $\mu_{r}^{0}$ to 0.005 and 1, respectively, to
restrict the learning from $\mathbb{D}_{U}$ since pseudo-labels are mostly
noisy during the early stages of training. As the training progresses and
overall reliability improves, we start learning from $\mathbb{D}_{U}$ by
gradually decreasing $\mu_{r}$. In addition, the change in $\mu_{r}$ is
directly controlled by the difficulty in learning the current batch of
samples. If the batch of samples at iteration $j$ is hard-to-learn, (_i.e_.
$d^{j}$ is high), we keep the change in $\mu_{r}$ to minimal. Similarly, we
exponentially decrease the contrastive loss coefficient $\mu_{c}$ as

$\mu_{c}^{j}=\mu_{c}^{j-1}e^{-\beta}.$ (11)

We set initial $\mu_{c}^{0}$ to 0.5 as unsupervised feature learning helps
more at early stages of training. $\beta$ is set to be 1e-4.

####  3.1.4 Semantic Segmentation

Up to now, we have only considered the classification task where each input
sample is associated with a single label. However, semantic segmentation is a
multi-label classification task where we assign a label to each pixel.
Consider a target domain image $x\in\mathbb{R}^{H\times W}$ where $x_{ij}$
indicates the pixel of $i^{th}$ row and $j^{th}$ column. The task at hand is
to assign one of $K$ semantic labels, $y_{ij}\in{1,2,\ldots K}$ to each
$x_{ij}$. For $x$, we use a model ($f$) to produce a probabilistic output
prediction $p\in\mathbb{R}^{H\times W\times K}$ over $K$ classes. The map of
pseudo-labels ($\hat{y}\in\mathbb{R}^{H\times W}$)can be estimated as
$\hat{y}=\operatorname*{arg\,max}_{k}p;k\in{1,2,\ldots K}$. As some
predictions are more reliable than others, using similar selection criteria
(as image classification) to separate pixels makes sense. However, instead of
using one single threshold for all pixels, we instead choose per-category
thresholds. To this end, we estimate a pair of thresholds for each category
$k$. Given a batch, we accumulate all confidence scores and select the per-
category confidence threshold, $\tau_{c}^{k}$, as the P-th percentile
confidence score. Similarly, we select P-th percentile uncertainty score for
the uncertainty threshold, $\tau_{c}^{k}$. In our work, we set the value of P
to 55. After choosing the thresholds, we follow eq. 4 to assign a per-pixel
reliability score, $r_{ij}$. As for loss functions, we consider cross-entropy
loss ($\mathcal{L}^{R}_{ce}$) for the reliable labels $\hat{y}^{R}$, and to
promote diverse predictions, we minimize the prediction entropy loss,

$\vspace{-0.5mm}\mathcal{L}_{E}=-\frac{1}{HW}\sum_{i=1,j=1}^{H,W}p_{ij}\cdot\log(p_{ij}).\vspace{-0.5mm}$
(12)

Finally, we update our model by minimizing the total loss,

$\mathcal{L}_{tot}=\mathcal{L}^{R}_{ce}+\mu_{e}\mathcal{L}_{E},$ (13)

Where $\mu_{e}$ is the entropy loss coefficient. We follow a similar update
equation as 11 for $\mu_{E}$ with an initial value of, $\mu^{0}_{e}$ = 1e-3.
Note that, we only update BN layers and freeze other parameters. For
uncertainty measures, we use ColorJitter and Gaussian noise as augmentation
transformations. More details are in the supplementary.

##  4 Experiments

###  4.1 Experimental Settings

Image Classification Datasets: _Office-31_ [60] is a small-scale benchmark
with images from 31 categories across 3 domains, Amazon (2,817), DSLR (498)
and Webcam (795). _Office-Home_ [71] has a total of 15.5K images from 65
classes collected from 4 different image domains: Artistic, Clipart, Product,
and Real-world. We consider 12 transfer tasks for this dataset. _VisDA_ [55]
contains 2 different domains, synthetic and real, with 12 classes in both
domains. The synthetic or source domain contains 150K rendered 3D images with
different poses. The corresponding real or target domain contains about 55K
real-world images. For evaluation, we consider per-class accuracy and the
average (Avg.) over them. _DomainNet_ [54] is another large-scale dataset with
6 domains containing over 500K images from 126 classes. We consider 4 domains
(Real, Sketch, Clipart, Painting), as [61] identify severe noisy labels in the
dataset. We evaluate the methods on 7 transfer tasks between 4 domains and
report top-1 accuracy.

Table 1: Classification accuracy (%) under UDA and SFDA settings on Office-31
dataset (ResNet50 backbone). We report Top-1 accuracy on 6 domain shifts
($\to$) and take the average (Avg.) over them. The best results under the SFDA
setting are shown in bold font.

Method |  | source
---
-free
A$\rightarrow$D | A$\rightarrow$W | D$\rightarrow$A | D$\rightarrow$W | W$\rightarrow$A | W$\rightarrow$D | Avg.
GSDA[24] | $\times$ | 94.8 | 95.7 | 73.5 | 99.1 | 74.9 | 100 | 89.7
CAN[31] | $\times$ | 95.0 | 94.5 | 78.0 | 99.1 | 77.0 | 99.8 | 90.6
SRDC[68] | $\times$ | 95.8 | 95.7 | 76.7 | 99.2 | 77.1 | 100 | 90.8
SHOT[42] | ✓ | 94.0 | 90.1 | 74.7 | 98.4 | 74.3 | 99.9 | 88.6
3C-GAN[37] | ✓ | 92.7 | 93.7 | 75.3 | 98.5 | 77.8 | 99.8 | 89.6
A2Net[79] | ✓ | 94.5 | 94.0 | 76.7 | 99.2 | 76.1 | 100 | 90.1
SFDA-DE [14] | ✓ | 96.0 | 94.2 | 76.6 | 98.5 | 75.5 | 99.8 | 90.1
C-SFDA (Ours) | ✓ | 96.2 | 93.9 | 77.3 | 98.8 | 77.9 | 99.7 | 90.5

Semantic Segmentation Datasets: For segmentation, we consider
GTA5$\to$Cityscapes, SYNTHIA$\to$Cityscapes & CityScapes$\to$Dark-Zurich
adaptations tasks. GTA5 [58] contains $\sim$25k synthetic images, with a
resolution of $1914\times 1052$, generated from GTA5 video frames. Cityscapes
[9] provides 3,975 daytime street scenes, with a resolution of $2048\times
1024$, from 50 different cities. Following prior work [23, 72, 93], we
consider splitting Cityscapes images into train-val splits and report 19-way
classification performance over the validation split. SYNTHIA [59] is another
synthetic dataset with 9400 scenes of size 1280x760. As SYNTHIA and Cityscapes
have overlaps only for 16 categories, we report 16-way and 13-way performances
for SYNTHIA$\to$Cityscapes. Dark Zurich [62] is a large dataset with 2,416
nighttime unlabeled images of 1080p resolution.

Table 2: Classification performance (%) under UDA and SFDA settings on Office-
Home dataset (ResNet50 backbone). We report Top-1 accuracy on 12 domain shifts
($\to$) and take the average (Avg.) over them. Our method achieves SOTA
performance on 8 of these shifts.

Method | SF | Ar$\rightarrow$Cl | Ar$\rightarrow$Pr | Ar$\rightarrow$Rw | Cl$\rightarrow$Ar | Cl$\rightarrow$Pr | Cl$\rightarrow$Rw | Pr$\rightarrow$Ar | Pr$\rightarrow$Cl | Pr$\rightarrow$Rw | Rw$\rightarrow$Ar | Rw$\rightarrow$Cl | Rw$\rightarrow$Pr | Avg.
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
RSDA[19] | $\times$ | 53.2 | 77.7 | 81.3 | 66.4 | 74.0 | 76.5 | 67.9 | 53.0 | 82.0 | 75.8 | 57.8 | 85.4 | 70.9
TSA[40] | $\times$ | 57.6 | 75.8 | 80.7 | 64.3 | 76.3 | 75.1 | 66.7 | 55.7 | 81.2 | 75.7 | 61.9 | 83.8 | 71.2
SRDC[68] | $\times$ | 52.3 | 76.3 | 81.0 | 69.5 | 76.2 | 78.0 | 68.7 | 53.8 | 81.7 | 76.3 | 57.1 | 85.0 | 71.3
FixBi[52] | $\times$ | 58.1 | 77.3 | 80.4 | 67.7 | 79.5 | 78.1 | 65.8 | 57.9 | 81.7 | 76.4 | 62.9 | 86.7 | 72.7
G-SFDA[85] | ✓ | 57.9 | 78.6 | 81.0 | 66.7 | 77.2 | 77.2 | 65.6 | 56.0 | 82.2 | 72.0 | 57.8 | 83.4 | 71.3
SHOT[42] | ✓ | 57.1 | 78.1 | 81.5 | 68.0 | 78.2 | 78.1 | 67.4 | 54.9 | 82.2 | 73.3 | 58.8 | 84.3 | 71.8
HCL [26] | ✓ | 64.0 | 78.6 | 82.4 | 64.5 | 73.1 | 80.1 | 64.8 | 59.8 | 75.3 | 78.1 | 69.3 | 81.5 | 72.6
A2Net[79] | ✓ | 58.4 | 79.0 | 82.4 | 67.5 | 79.3 | 78.9 | 68.0 | 56.2 | 82.9 | 74.1 | 60.5 | 85.0 | 72.8
SFDA-DE [14] | ✓ | 59.7 | 79.5 | 82.4 | 69.7 | 78.6 | 79.2 | 66.1 | 57.2 | 82.6 | 73.9 | 60.8 | 85.5 | 72.9
C-SFDA (Ours) | ✓ | 60.3 | 80.2 | 82.9 | 69.3 | 80.1 | 78.8 | 67.3 | 58.1 | 83.4 | 73.6 | 61.3 | 86.3 | 73.5

Table 3: Source-free (SF) domain adaptation performance on VisDA dataset
(ResNet-101 backbone) shown by per-class accuracy (%) and their average
(Avg.). Our method improves the average accuracy by 1% compared to the
previous SOTA, Adacon [4].

Method | SF | plane | bike | bus | car | horse | knife | mcycle | person | plant | sktbrd | train | truck | Avg.
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
MCC[30] | $\times$ | 88.7 | 80.3 | 80.5 | 71.5 | 90.1 | 93.2 | 85.0 | 71.6 | 89.4 | 73.8 | 85.0 | 36.9 | 78.8
STAR[48] | $\times$ | 95.0 | 84.0 | 84.6 | 73.0 | 91.6 | 91.8 | 85.9 | 78.4 | 94.4 | 84.7 | 87.0 | 42.2 | 82.7
RWOT[81] | $\times$ | 95.1 | 80.3 | 83.7 | 90.0 | 92.4 | 68.0 | 92.5 | 82.2 | 87.9 | 78.4 | 90.4 | 68.2 | 84.0
SE[18] | $\times$ | 95.9 | 87.4 | 85.2 | 58.6 | 96.2 | 95.7 | 90.6 | 80.0 | 94.8 | 90.8 | 88.4 | 47.9 | 84.3
Source only | - | 57.2 | 11.1 | 42.4 | 66.9 | 55.0 | 4.4 | 81.1 | 27.3 | 57.9 | 29.4 | 86.7 | 5.8 | 43.8
SHOT[42] | ✓ | 94.3 | 88.5 | 80.1 | 57.3 | 93.1 | 94.9 | 80.7 | 80.3 | 91.5 | 89.1 | 86.3 | 58.2 | 82.9
A2Net[79] | ✓ | 94.0 | 87.8 | 85.6 | 66.8 | 93.7 | 95.1 | 85.8 | 81.2 | 91.6 | 88.2 | 86.5 | 56.0 | 84.3
SFDA-DE [14] | ✓ | 95.3 | 91.2 | 77.5 | 72.1 | 95.7 | 97.8 | 85.5 | 86.1 | 95.5 | 93.0 | 86.3 | 61.6 | 86.5
AdaCon [4] | ✓ | 97.0 | 84.7 | 84.0 | 77.3 | 96.7 | 93.8 | 91.9 | 84.8 | 94.3 | 93.1 | 94.1 | 49.7 | 86.8
C-SFDA (Ours) | ✓ | 97.6 | 88.8 | 86.1 | 72.2 | 97.2 | 94.4 | 92.1 | 84.7 | 93.0 | 90.7 | 93.1 | 63.5 | 87.8
AdaCon [4] (Online) | ✓ | 95.0 | 68.0 | 82.7 | 69.6 | 94.3 | 80.8 | 90.3 | 79.6 | 90.6 | 69.7 | 87.6 | 36.0 | 78.7
C-SFDA (Online) | ✓ | 95.9 | 75.6 | 88.4 | 68.1 | 95.4 | 86.1 | 94.5 | 82.0 | 89.2 | 80.2 | 87.3 | 43.8 | 82.1

Implementation Details: We use ResNet50 [22] backbone for Office-31, Office-
Home, DomainNet and ResNet-101 [22] for VisDA. Following SHOT [42], we replace
the fully connected (FC) layer with a 256-dimensional bottleneck layer and
task-specific FC classification layer. We use batch normalization [29] after
bottleneck and apply WeightNorm [63] on the classifier. For source training,
we initialize the models with ImageNet-1K [13] pre-trained weights. Following
[42], we split the source dataset into the train (90%) and validation (10%)
sets. We employ a 10 times higher learning rate for bottleneck and classifier
than the backbone. For target domain adaptation, we use similar training
settings for all the datasets. For Office datasets [60, 71], we use SGD
optimizer with a learning rate of 5e-3, a momentum of 0.9 with a weight decay
of 1e-4 and a batch size of 128. For VisDA and DomainNet, we use a learning
rate of 5e-4 with cosine annealing [4]. We train for 20 epochs with a batch
size of 128 for VisDA. We consider a larger batch size of 256 for DomainNet
and train for 25 epochs. We set $\eta$ to 0.98 for EMA updates across
datasets.

For all semantic segmentation, we use DeepLabV2 [6] with a ResNet101 [22]
backbone and initialize models with ImageNet-1K [13] pre-trained weights. For
GTA5 and SYNTHIA source training, we use an SGD optimizer with a 1e-4 learning
rate, a 0.9 momentum, and a weight decay of 5e-4. We train the model for 20
epochs with a batch size of 8 and apply different weather augmentations [50]
during training. For Cityscapes, we follow the settings in [62] and use a
learning rate of 2.5e-4. During adaptation, we use a learning rate of 1e-4 to
tune only the batch normalization (BN) parameters. With a batch size of 8, we
train the model for 50K steps. Note that we only consider online adaptation
for Cityscapes$\to$Dark-Zurich and train the model for 1 epoch. Similar to
Image classification, we also consider EMA update for segmentation and set
$\eta$ to $0.995$. Please see supplementary for more details.

###  4.2 Experimental Results

Evaluation on Image Classification Task: We compare the proposed method on
Image Classification benchmarks in Table 1-4. We report the Top-1 accuracy for
each domain shift and take their average. For Office-31 dataset, we achieve an
average 0.4% accuracy improvement over the previous SOTA. We also achieve a
similar improvement (0.6%) for the Office-Home dataset. We believe, avoiding
the early training time label noise propagation, helps our method
significantly to perform well. In VisDA, C-SFDA outperforms SOTA AdaCon [4] by
1% and obtains significant performance improvement for the rare classes such
as ”truck”. Table 4 shows that the proposed method sees similar accuracy
improvement (1.2%) over the previous SOTA for DomainNet. Although AdaCon [4]
uses a large memory queue to refine the pseudo-labels, it still suffers from
early training time memorization. Whereas utilizing a _label-selection
technique_ for curriculum training, C-SFDA eliminates the requirement of a
_label-refinement technique_ and still outperforms AdaCon [4]. We also
consider several general UDA techniques considering continued source data
access. We encouragingly find that the proposed C-SFDA performs better than
most of these methods across datasets, even without source data access.

Table 4: Classification accuracy (%) on DomainNet for source-free domain
adaptation (ResNet-50 backbone).

Method | SF | R$\to$C | R$\to$P | P$\to$C | C$\to$S | S$\to$P | R$\to$S | P$\to$R | Avg.
---|---|---|---|---|---|---|---|---|---
MCC [30] | $\times$ | 44.8 | 65.7 | 41.9 | 34.9 | 47.3 | 35.3 | 72.4 | 48.9
Source only | - | 55.5 | 62.7 | 53.0 | 46.9 | 50.1 | 46.3 | 75.0 | 55.6
TENT [73] | ✓ | 58.5 | 65.7 | 57.9 | 48.5 | 52.4 | 54.0 | 67.0 | 57.7
SHOT [42] | ✓ | 67.7 | 68.4 | 66.9 | 60.1 | 66.1 | 59.9 | 80.8 | 67.1
AdaCon [4] | ✓ | 70.2 | 69.8 | 68.6 | 58.0 | 65.9 | 61.5 | 80.5 | 67.8
Ours | ✓ | 70.8 | 71.1 | 68.5 | 62.1 | 67.4 | 62.7 | 80.4 | 69.0
AdaCon [4] (online) | ✓ | 61.1 | 66.9 | 60.8 | 53.4 | 62.7 | 54.5 | 78.9 | 62.6
Ours (online) | ✓ | 61.6 | 67.4 | 61.3 | 55.1 | 63.2 | 54.8 | 78.5 | 63.1

Table 5: Performance evaluation on GTA5$\to$Cityscapes (DeepLabV2 with
ResNet101) where we report mean IoU (mIoU) over 19 categories on Cityscapes
validations set. Our method achieves the best mIoU in SFDA and online test-
time adaptation.

Method | SF | Road | SW | Build | Wall | Fence | Pole | TL | TS | Veg. | Terrain | Sky | PR | Rider | Car | Truck | Bus | Train | Motor | Bike | mIoU
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
CrCDA [27] | $\times$ | 92.4 | 55.3 | 82.3 | 31.2 | 29.1 | 32.5 | 33.2 | 35.6 | 83.5 | 34.8 | 84.2 | 58.9 | 32.2 | 84.7 | 40.6 | 46.1 | 2.1 | 31.1 | 32.7 | 48.6
ProDA [87] | $\times$ | 91.5 | 52.4 | 82.9 | 42.0 | 35.7 | 40.0 | 44.4 | 43.3 | 87.0 | 43.8 | 79.5 | 66.5 | 31.4 | 86.7 | 41.1 | 52.5 | 0.0 | 45.4 | 53.8 | 53.7
CPSL [38] | $\times$ | 91.7 | 52.9 | 83.6 | 43.0 | 32.3 | 43.7 | 51.3 | 42.8 | 85.4 | 37.6 | 81.1 | 69.5 | 30.0 | 88.1 | 44.1 | 59.9 | 24.9 | 47.2 | 48.4 | 55.7
Source Only | - | 69.7 | 20.5 | 73.3 | 22.1 | 12.3 | 23.5 | 31.8 | 17.9 | 78.7 | 18.7 | 68.2 | 53.9 | 26.5 | 70.6 | 32.2 | 4.5 | 8.1 | 26.8 | 31.5 | 36.4
UR [66] | ✓ | 92.3 | 55.2 | 81.6 | 30.8 | 18.8 | 37.1 | 17.7 | 12.1 | 84.2 | 35.9 | 83.8 | 57.7 | 24.1 | 81.7 | 27.5 | 44.3 | 6.9 | 24.1 | 40.4 | 45.1
SFDA [46] | ✓ | 91.7 | 52.7 | 82.2 | 28.7 | 20.3 | 36.5 | 30.6 | 23.6 | 81.7 | 35.6 | 84.8 | 59.5 | 22.6 | 83.4 | 29.6 | 32.4 | 11.8 | 23.8 | 39.6 | 45.8
HCL [26] | ✓ | 92.0 | 55.0 | 80.4 | 33.5 | 24.6 | 37.1 | 35.1 | 28.8 | 83.0 | 37.6 | 82.3 | 59.4 | 27.6 | 83.6 | 32.3 | 36.6 | 14.1 | 28.7 | 43.0 | 48.1
C-SFDA (ours) | ✓ | 90.4 | 42.2 | 83.2 | 34.0 | 29.3 | 34.5 | 36.1 | 38.4 | 84.0 | 43.0 | 75.6 | 60.2 | 28.4 | 85.2 | 33.1 | 46.4 | 3.5 | 28.2 | 44.8 | 48.3
AUGCO [56] (Online) | ✓ | 90.3 | 41.2 | 81.8 | 26.5 | 21.4 | 34.5 | 404. | 33.3 | 83.6 | 34.6 | 79.7 | 61.4 | 19.3 | 84.7 | 30.3 | 39.5 | 7.3 | 27.6 | 34.6 | 45.9
C-SFDA (Online) | ✓ | 84.7 | 37.8 | 82.4 | 29.7 | 28.0 | 31.8 | 34.8 | 29.3 | 83.7 | 43.8 | 76.9 | 58.8 | 28.4 | 84.9 | 33.5 | 44.1 | 0.5 | 24.5 | 39.1 | 46.3

Table 6: Performance evaluation on SYNTHIA$\to$Cityscapes. We report mean IoU
(mIoU) over 16 common categories between SYNTHIA and Cityscapes. mIoU* are
calculated over 13 categories. Our method achieves SOTA performance in both
mIoU and mIoU*.

Method | SF | Road | SW | Build | Wall* | Fence* | Pole* | TL | TS | Veg. | Sky | PR | Rider | Car | Bus | Motor | Bike | mIoU | mIoU*
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
CrCDA[27] | $\times$ | 86.2 | 44.9 | 79.5 | 8.3 | 0.7 | 27.8 | 9.4 | 11.8 | 78.6 | 86.5 | 57.2 | 26.1 | 76.8 | 39.9 | 21.5 | 32.1 | 42.9 | 50.0
ProDA [87] | $\times$ | 87.1 | 44.0 | 83.2 | 26.9 | 0.7 | 42.0 | 45.8 | 34.2 | 86.7 | 81.3 | 68.4 | 22.1 | 87.7 | 50.0 | 31.4 | 38.6 | 51.9 | 58.5
CPSL [38] | $\times$ | 87.3 | 44.4 | 83.8 | 25.0 | 0.4 | 42.9 | 47.5 | 32.4 | 86.5 | 83.3 | 69.6 | 29.1 | 89.4 | 52.1 | 42.6 | 54.1 | 54.4 | 61.7
Source Only | - | 45.2 | 19.6 | 72.0 | 6.7 | 0.1 | 24.3 | 5.5 | 7.8 | 74.4 | 81.9 | 57.3 | 17.3 | 39.0 | 19.5 | 7.0 | 6.2 | 31.3 | 36.2
UR [66] | ✓ | 59.3 | 24.6 | 77.0 | 14.0 | 1.8 | 31.5 | 18.3 | 32.0 | 83.1 | 80.4 | 46.3 | 17.8 | 76.7 | 17.0 | 18.5 | 34.6 | 39.6 | 45.0
SFDA [46] | ✓ | 67.8 | 31.9 | 77.1 | 8.3 | 1.1 | 35.9 | 21.2 | 26.7 | 79.8 | 79.4 | 58.8 | 27.3 | 80.4 | 25.3 | 19.5 | 37.4 | 42.4 | 48.7
HCL [26] | ✓ | 80.9 | 34.9 | 76.7 | 6.6 | 0.2 | 36.1 | 20.1 | 28.2 | 79.1 | 83.1 | 55.6 | 25.6 | 78.8 | 32.7 | 24.1 | 32.7 | 43.5 | 50.2
C-SFDA (Ours) | ✓ | 87.0 | 39.0 | 79.5 | 12.2 | 1.8 | 32.2 | 20.4 | 24.3 | 79.5 | 82.2 | 51.5 | 24.5 | 78.7 | 31.5 | 21.3 | 47.9 | 44.6 | 51.3
AUGCO [56] (Online) | ✓ | 74.8 | 32.1 | 79.2 | 5.0 | 0.1 | 29.4 | 3.0 | 11.1 | 78.7 | 83.1 | 57.5 | 26.4 | 74.3 | 20.5 | 12.1 | 39.3 | 39.2 | 45.5
C-SFDA (Online) | ✓ | 85.9 | 38.1 | 79.2 | 11.9 | 1.1 | 32.0 | 17.1 | 22.9 | 79.7 | 89.4 | 46.6 | 22.0 | 78.4 | 29.6 | 17.4 | 46.0 | 43.0 | 49.5

Table 7: Evaluation on Cityscapes$\to$Dark-Zurich. We report mean IoU (mIoU)
over 19 common categories between theses datasets.

Method | Source | TTBN [53] | TENT [73] | AUGCO [56] | C-SFDA (Ours)
---|---|---|---|---|---
mIoU | 28.8 | 28.0 | 26.6 | 32.4 | 33.2

Evaluation on Semantic Segmentation Task: Table 5 shows the performance on
GTA5$\to$Cityscapes. For this adaptation, we resize the target scenes to
$1024\times 512$ and use DeepLabV2 for training. We choose this common
architecture to be consistent with other recent works. The proposed method
outperforms the state-of-the-art SFDA method HCL [26] with 19-way averaged
mIoU of 48.3%. Note that, some classes in Cityscapes have very low initial
pixel-level accuracy, _e.g_. _Train_ category, and it is challenging to obtain
satisfactory performance even with selective pseudo-labeling. It requires
mentioning that HCL [26] employs historical contrastive loss enforcing
additional memory overhead; an undesirable property in most adaptation
scenarios. On the other hand, our method utilizes a simple pixel-level
prediction reliability measure which is highly computationally efficient and
leads to the best mIoU. As regular UDA techniques have the advantage of source
data access and most employ highly sophisticated techniques specific to
semantic segmentation, they usually perform better than SFDA techniques.
However, we find C-SFDA performs comparably to several UDA techniques, _e.g_.
CrCDA [27]. We also evaluate SYNTHIA$\to$Cityscapes (Table 6) benchmark, where
we use the same DeepLabV2 architecture and adaptation strategy. Compared to
the baselines, C-SFDA performs significantly better, with a mIoU improvement
of 1.1% over the previous SOTA.

Compatibility to Online Adaptation: As C-SFDA employs batch-wise selection
instead of using the whole dataset, it is readily applicable to online fully
test-time domain adaptation [73, 56]. In contrast to regular SFDA experiments,
we only train the model for 1 epoch following prior works [73, 56], without
any change to our training settings. In both image classification and semantic
segmentation experiments, C-SFDA performs better than previous state-of-the-
art methods (Table 3 -7). For instance, we achieve a $3.5\%$ accuracy gain in
VisDA image classification (Table 3). In segmentation, we improve the mIoU by
$0.8\%$ in Cityscapes$\to$Dark Zurich (Table 7) and $4\%$ in
SYNTHIA$\to$Cityscapes (Table 6). These gains can be attributed to the
adoption of high-quality pseudo-labels right from the beginning of the
training. Learning in this manner gives us a head start in producing reliable
pseudo-labels for subsequent self-training.

Figure 3: Training statistics for VisDA Dataset. As the training progresses,
(a) average confidence score increases, (b) average uncertainty score
decreases, (c) C-SFDA selects more samples as reliable. (d) Among these
selected labels for $\mathbb{D}_{R}$, most of them are accurate as shown by
the pseudo-label accuracy. (e) By putting more weight on the cross-entropy
loss, we first learn from $\mathbb{D}_{R}$ and then learn from
$\mathbb{D}_{U}$ by minimizing propagation loss. Contrastive loss is minimized
throughout the training for representation learning. (f) Average (Avg.)
accuracy improves significantly.

####  4.2.1 Ablation Study

Does Traditional Label Noise Learning Help? Since our method deals with noisy
labels, we explore the literature on label noise learning (LNL) and apply them
in the SFDA setting. We consider 3 widely used techniques: GCE [89], PCL [88],
ELR [45] along with regular cross-entropy loss with all pseudo labels and
compare their performance on 3 datasets. Fig. 4(b) shows that traditional LNL
techniques may not be suitable for SFDA as they severely underperform compared
to C-SFDA. One possible reason could be that SFDA contains unbounded label
noise due to an unknown domain shift. In the case of _unbounded label noise_ ,
noise rates are unknown and can be very high; which is in contrast to the
general belief of _bounded label noise_ where noise rate and type are known
priors. In such a scenario, traditional LNL methods struggle to curate label
noise. Our method can convincingly perform well in this scenario without any
prior knowledge of noise type, rate, _etc_.

Table 8: Ablation study with different components of our proposed method.
Contrastive learning along with uncertainty plays a vital role in achieving
SOTA average accuracy (%) in image classification benchmarks.

Selection Strategy | Label Bal. | Loss | Accuracy (%)
---|---|---|---
Conf. | Unc. | DoC | $\lambda_{k}$ | $\mathcal{L}_{ce}^{R}$ | $\mathcal{L}_{\mathcal{P}}$ | $\mathcal{L}_{\mathcal{C}}$ | Office-31 | Office-Home | VisDA | DomainNet
Self-training ($\mathcal{L}_{ce}$) with all pseudo-labels | 81.1 | 62.3 | 57.2 | 52.6
✓ |  |  | ✓ | ✓ | ✓ | ✓ | 87.6 | 69.2 | 85.2 | 65.5
✓ | ✓ |  | ✓ | ✓ | ✓ | ✓ | 89.9 | 71.8 | 87.4 | 68.7
✓ | ✓ | ✓ | $\times$ | ✓ | ✓ | ✓ | 90.1 | 73.3 | 86.5 | 68.3
✓ | ✓ | ✓ | ✓ | ✓ | $\times$ | $\times$ | 88.7 | 71.6 | 85.9 | 67.3
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | $\times$ | 88.9 | 72.3 | 86.4 | 67.9
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 90.5 | 73.5 | 87.8 | 69.0

Effect of Different Selection Criteria: Table 8 shows the ablation study with
different elements of our proposed method. We first show the performance
without curriculum learning where we use all pseudo-labels for fully
supervised training with CE loss. Since implementing the curriculum learning
requires pseudo-label selection, we analyze the impact of confidence,
uncertainty, and DoC here first. It can be observed that each of these metrics
can have a significant impact on the overall performance, especially
_prediction uncertainty_. As the choice of augmentations plays a vital role in
measuring uncertainty, we conduct a detailed study on different augmentation
policies (details in supplementary). In Fig. 4(a), we also analyze the impact
of augmentations number $L$ on overall classification performance.

Effect of Different Loss Functions: We also analyze the impact of different
loss functions in Table 8. It can be seen that using only CE loss produces
quite satisfactory performance. This indicates learning only from reliable
samples is good enough for SFDA. Interestingly, applying propagation loss
without contrastive loss may experience performance degradation as we are
still using label information. This underlines the importance of CL in
preventing label noise memorization.

Table 9: Effect of Curriculum

Dataset | VisDA-C | DomainNet
---|---|---
Curr. | 87.8 | 69.0
w/o Curr. | 87.1 | 68.6

We also show the impact of label balancing here which is only being considered
for CE loss. In Figure 3, we show some important training statistics of C-SFDA
for VisDA dataset.

Effect of Curriculum: In Table 9, we show the impact of curriculum on the
performance of our proposed method.

(a) Effect of Number of Augmentations

(b) LNL Techniques in SFDA

Figure 4: (a) Ablation with different $L$ shows that we need to consider a
sufficient number of augmentations for measuring prediction uncertainty as it
plays a crucial role in obtaining SOTA average accuracy. (b) Performance of
SOTA noisy label learning methods in SFDA. Due to the presence of unbounded
label noise (_i.e_. high noise rates and unknown noise types), traditional LNL
struggles to perform well in SFDA settings.

##  5 Conclusion

In this work, we introduce a novel source-free domain adaptation technique
exploiting the phenomenon of early memorization of noisy pseudo-labels. Due to
the inevitable presence of this phenomenon, we employ a curriculum learning-
aided selective self-training strategy that prioritizes learning from highly
reliable pseudo-labels and propagating label information to less reliable
ones. This leads us to a hyper-parameter independent label selection technique
that replaces the need for a label refinement technique. In addition, we
utilize contrastive loss-based representation learning that helps generate
consistent feature representation and guides the overall adaptation better.
Due to the memory-efficient property of our method, C-SFDA can easily be
employed for online test-time domain adaptation scenarios. Extensive
evaluations show the superior performance of our method on a wide range of
image classification and semantic segmentation benchmarks.

Limitations: We propose to utilize the reliability of generated labels in
selective pseudo-labeling. Depending on the domain shift, the initial
reliability often varies and can be severely misleading if the domain shift is
too large. Such scenarios may require additional measures such as label noise
robust self-supervised learning or strongly augmented source domain training.
However, we expect our proposed method to be over-restrictive in selecting
pseudo-labels whenever such an extreme situation appears.

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