# Re-energizing Domain Discriminator with Sample Relabeling
for Adversarial Domain Adaptation

Xin Jin1 Cuiling Lan2 Wenjun Zeng2 Zhibo Chen1∗
1 University of Science and Technology of China 2 Microsoft Research Asia,
Beijing, China
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>Corresponding Author.

###### Abstract

Many unsupervised domain adaptation (UDA) methods exploit domain adversarial
training to align the features to reduce domain gap, where a feature extractor
is trained to fool a domain discriminator in order to have aligned feature
distributions. The discrimination capability of the domain classifier w.r.t.
the increasingly aligned feature distributions deteriorates as training goes
on, thus cannot effectively further drive the training of feature extractor.
In this work, we propose an efficient optimization strategy named Re-
enforceable Adversarial Domain Adaptation (RADA) which aims to re-energize the
domain discriminator during the training by using dynamic domain labels.
Particularly, we relabel the well aligned target domain samples as source
domain samples on the fly. Such relabeling makes the less separable
distributions more separable, and thus leads to a more powerful domain
classifier w.r.t. the new data distributions, which in turn further drives
feature alignment. Extensive experiments on multiple UDA benchmarks
demonstrate the effectiveness and superiority of our RADA.

Figure 1: Motivation and our idea. (a) Previous methods align domain
distributions by adversarially training the domain classifier with static
domain labels of the samples. The discrimination capability of the domain
classifier w.r.t. the increasingly aligned feature distributions deteriorates
as training goes on (i.e., more samples are inseparable), which in turn
provides less driving power to the feature extractor for alignment and
prevents effective optimization. (b) We propose a new solution which allows
dynamic domain labels. We relabel the “well aligned” target samples as source
domain, which makes the two less separable distributions more separable and
thus leads to a more powerful domain classifier, which in turn further drives
feature alignment.

##  1 Introduction

The rapid development of deep learning has helped achieve significant
improvements for many computer vision tasks. Those learned models usually have
high performance on the test data that share similar characteristics as the
training data, but suffer from significant performance degradation when
deployed in new environments [11, 13, 25]. There are _domain gaps_ between the
training source data (source domain) and the testing target data (target
domain). Besides, annotating the data for the target domain is expensive and
time-consuming. To mitigate the notorious domain gap issue without manual
annotation efforts, unsupervised domain adaptation (UDA) [11, 13, 19, 22, 63,
26, 9] is recently extensively explored, which aims to learn from labeled
source domain and unlabeled target domain.

For domain adaptation, theoretical analysis by Ben-David  _et al_. [1] shows
that reducing the feature differences between the source and target domains
can reduce the upper bound of the target domain error. Many domain adaptation
methods tend to learn domain invariant/transferable feature representations
[15, 17, 14, 34]. Inspired by the Generative Adversarial Networks (GANs) [16],
adversarial learning has been successfully applied for UDA [13, 45, 7, 42, 48,
30, 61, 10]. The core idea of adversarial domain adaptation approaches is to
train a domain discriminator/classifier to distinguish between the source and
target domains and train the generator/feature extractor to minimize the
feature discrepancy between the source and target domain in order to fool the
discriminator in a minmax two-player game. High discrimination capability for
the domain discriminator is desired in order to be able to drive the feature
alignment. Typically, a domain adversarial neural network (DANN) [13]
introduces a gradient reversal layer (GRL) for adversarial training, where the
ordinary gradient descent is applied for optimizing the domain classifier and
the sign of the gradient is reversed when passing through the GRL to optimize
the feature extractor. Conditional domain adversarial network (CDAN) [30]
improves DANN by conditioning the domain discriminator on both the object
classification predictions/likelihoods and the extracted features.

All these methods train the domain discriminator with the static domain labels
of the source and target samples. However, as shown in Figure 1 (a), as the
adversarial training goes on, the feature distributions for the source domain
and target domain are increasingly aligned. The discrimination capability of
the domain discriminator/classifier w.r.t. the more aligned distributions is
weaker than that w.r.t. the earlier less aligned distributions. Such
degradation of discrimination capability in turn provides less driving power
to the feature extractor for alignment, hindering effective optimization, even
though there are still not aligned samples in the feature space.

To better understand what is happening, on top of a representative baseline
scheme CDAN [30], we observe the variation of the discrimination capability of
the domain discriminator by calculating the average entropy of domain
classification for all the training samples at each training epoch (see Figure
2 (a)), where an epoch is a single pass through the full training set. As we
know, a larger entropy of domain classification (i.e., the discriminator has
larger ambiguity on the domain classes of the samples) indicates a poorer
discrimination capability of the domain classifier. We also observe how well
the alignment is by calculating a domain discrepancy measurement Maximum Mean
Discrepancy (MMD) [29, 2, 54] (see Figure 2 (b)).

Figure 2 (a) reveals that the average entropy of the baseline scheme goes
through a process of fast decreasing and then slowly rises with fluctuation.
1) In the early epochs, the successive training of domain classifier increases
its discrimination power and thus the entropy decreases. 2) Meanwhile, as the
training goes on, the feature distributions for the source domain and target
domain are increasingly aligned, i.e., the domain discrepancy decreases as
shown in Figure 2 (b). This could adversarially increase the entropy. 3) The
optimization progresses/paces of the feature extractor and the domain
discriminator are usually not the same. Around 5 to 15 epochs, the domain
discrepancy continues to decrease even when the discrimination capability
reduces (i.e., entropy increases). This may be because the optimization of
feature extractor lags behind that of the domain discriminator and thus the
feature extractor can be further optimized. 4) The discrimination capability
of the domain classifier w.r.t. the more aligned distributions is becoming
weaker than that w.r.t. the earlier less aligned distributions (the entropy
increases). The persistent degradation in turn provides less driving power to
the feature extractor for alignment, hindering effective optimization (where
the alignment state cannot be effectively improved after 15 epochs, please
refer to the green curve in Figure 2 (b)). Thus, a mechanism that could
enhance/improve on the fly the discrimination capability of the domain
classifier to re-energize the adversarial training is highly desired.

Figure 2: The variation of (a) the discrimination capability of the domain
discriminator (measured by entropy of domain classification) and (b) alignment
state (measured by domain discrepancy measure of MMD) in the training. For the
baseline scheme CDAN [30] (marked by green), the discrimination capability of
the domain discriminator deteriorates w.r.t. the gradually aligned
distributions after the initial dip of the entropy, which in turn provides
less driving power to the feature extractor for alignment. In contrast, thanks
to our strategy of re-labeling the aligned target samples as source samples,
our scheme (marked by red) could improve the discrimination capability of the
domain discriminator (i.e., preventing the increasing of the entropy) and thus
in turn further drives feature alignment. These experiments are conducted on
Office-31 of the setting W$\rightarrow$A. Note that more analysis can be found
in our Supplementary.

Motivated by this, in this paper, we propose a new optimization strategy named
Re-enforceable Adversarial Domain Adaptation (RADA) which aims to re-energize
the domain discriminator/classifier during the training. Particularly, instead
of training the domain classifier using static domain labels for the source
and target samples (see Figure 1 (a)), we propose to exploit dynamic domain
labels, where we relabel the “well aligned” target samples, i.e., those close
to source domain, as source domain samples, in each mini-batch to be
optimized/trained. As illustrated in Figure 1 (b), this makes the two less
separable distributions more separable and thus leads to a more powerful
domain discriminator. Such re-energized domain discriminator in turn further
drives the feature alignment for the feature extractor. From Figure 2, we can
observe that after using our proposed data relabeling strategy, the domain
classification entropy is much lower than that of the baseline scheme and the
domain discrepancy is also smaller.

We summarize our main contributions as follows:

  * •

We pinpoint that the popular adversarial domain adaptation approaches in
general face optimization difficulty, which is caused by the deteriorated
discrimination capability of the domain classifier as the feature
distributions become increasingly aligned in training.

  * •

To alleviate the deterioration of the discrimination capability of the domain
discriminator, we propose an efficient optimization strategy named Re-
enforceable Adversarial Domain Adaptation (RADA), which is capable of re-
energizing the domain discriminator during training which in turn further
drives feature alignment. We achieve this by relabeling the well aligned
target samples as source domain samples for online training of the domain
discriminator.

We will demonstrate the effectiveness of the proposed RADA on top of multiple
widely-used adversarial learning based domain adaptation baselines. RADA
significantly outperforms the state-of-the-art UDA approaches. RADA is simple
yet effective and can be used as a _plug-and-play_ optimization strategy for
existing adversarial learning based UDA approaches. Note that we do not make
any change to the network architecture of the domain discriminator, which
makes the RADA friendly to many adversarial domain adaptation methods. We will
release our code upon acceptance.

##  2 Related Work

Unsupervised Domain Adaptation (UDA). UDA has achieved great progress with the
advancement of deep learning [11, 13, 19, 22, 26, 9]. These UDA methods could
be mainly categorized into three classes: pixel-level translation based
methods [5, 4, 28], explicit domain alignment based methods [17, 29, 31, 43,
54], and adversarial learning based methods [13, 45, 7, 42, 48, 30, 61, 10].
The third category predominates in recent years due to their superior
performance and we will focus and elaborate on them.

Inspired by Generative adversarial networks (GANs) [16], adversarial domain
adaptation has been widely explored [7, 42, 48]. The core idea of adversarial
UDA approaches is to train a domain discriminator to distinguish the source
and target domains and train a feature extractor to learn domain-invariant
features to fool the discriminator. Domain Adversarial Neural Network (DANN)
[13] is a representative work, where a domain classifier is connected to the
feature extractor via a gradient reversal layer (GRL). Subsequently, many
well-designed adversarial UDA variants are proposed, such as ADDA [45], CyCADA
[21], SBADA [39], CDAN [30], MCD [41], MSTN [51] and TADA [49]. Symnets [61]
builds a symmetric design of source and target task classifiers, on which the
domain discrimination and domain confusion training are based. TADA [49] is
also built upon adversarial domain adaptation framework, which adds
adversarial alignment constraints on both of transferable local regions and
global images through two local/global attention modules. MSTN [51] improves
the adversarial UDA framework by additionally leveraging clustering technique
to align labeled source centroid and pseudo-labeled target centroid, where
features in the same class but different domains are expected to be mapped to
nearby. Similarly, PFAN [6] performs feature alignment between category-wise
prototypes, with the number of target domain samples progressively increasing.
CDAN [30] proposes to condition the domain discriminator on the discriminative
information conveyed in the classifier predictions (class likelihood).
Besides, for safe transfer, it prioritizes over those easy-to-transfer samples
with confident predictions of object classes (measured by the entropy) by
reweighting each sample by an entropy-aware weight. Recently, Cui  _et al_.
[10] equip the adversarial adaptation framework with a Gradually Vanishing
Bridge (GVB) mechanism, which reduces the transfer difficulty by gradually
reducing the domain-specific characteristics in domain-invariant
representations. CMSS [55] introduces an agent to learn a dynamic curriculum
to softly select/re-weight source samples that are best suited for aligning to
the target domain.

As the optimization of the minmax game in domain adversarial adaptation
proceeds, the increasingly aligned feature distributions would degrade the
discrimination capability of the domain classifier, which reduces the driving
power for the feature alignment. In this paper, we propose to relabel the well
aligned target samples as source samples to make the less separable
distributions more separable, re-energizing the entire adversarial
optimization.

Mixup Technique. Mixup, which mixes two samples and their labels to generate a
new sample, as a data augmentation/regularization technique, was first
introduced in [58] and was widely applied in deep learning pipelines to
improve performance [20, 8, 57, 18]. The study of using Mixup for domain
adaptation is rare [52, 50] and remains an open problem. Xu  _et al_. [52]
present adversarial domain adaptation with domain mixup (DM-ADA) to guarantee
domain-invariance in a more continuous latent space, where two samples (one
from source domain and the other from target domain) and their domain labels
are softly mixed to explore inter-domain information. Wu  _et al_. [50]
propose a dual mixup regularized learning (DMRL) method for UDA, which jointly
conducts category and domain mixup regularization on the pixel level to guide
the object classifier in enhancing consistent predictions in-between samples
and enrich the intrinsic structures of the latent space. These mixup methods
aim to explore inter-domain information. In contrast, we aim to enhance the
continuity within the source domain after the re-partition of domains, where
the samples to be mixed share the same source domain labels.

##  3 Re-enforceable Adversarial Domain Adaptation (RADA)

Figure 3: Illustration of our Re-enforceable Adversarial Domain Adaptation
(RADA) optimization using toy examples. (a) Initial feature distributions of
source and target data sets. (b) As the online adversarial training goes on,
the source and target distributions are increasingly aligned. Some samples are
already aligned such that the domain classifier cannot distinguish them well
while some other samples are still not aligned. The ambiguous samples
deteriorate the discrimination capability of the domain classifier. (c) We
relabel the well aligned target samples as source domain samples, which makes
the less separable distributions more separable. With this strategy, we have a
re-enforceable domain classifier, which further re-energizes the domain
adversarial optimization in (d).

We propose an efficient domain adaptation optimization strfategy, named Re-
enforceable Adversarial Domain Adaptation (RADA), which re-energizes the
domain classifier/discriminator during the adversarial training by using
sample domain re-labeling strategy. Figure 3 illustrates the main procedure of
our RADA optimization. As the adversarial training goes on, the source and
target distributions are increasingly aligned. As illustrated in Figure 3 (b),
some samples are already aligned such that the domain classifier cannot
distinguish them well while some other samples are still not aligned. As
revealed in Figure 2, the discrimination capability of the domain classifier
(given a fixed model size) w.r.t. the more aligned distributions deteriorates
since the aligned samples are inseparable, which in turn results in less
driving power for the feature alignment.

We re-energize this domain discriminator by relabeling the well-aligned target
domain samples as source domain, leading to a clear boundary for the two new
distributions. Particularly, as illustrated in Figure 3 (c), we relabel the
well aligned target samples as source domain samples and train the domain
classifier under the updated partition. To determine which target samples are
to be relabeled in the training, we use a metric to evaluate how well a target
sample is aligned to the source domain, by simply taking into account the
certainty (entropy) of the prediction of the domain classifier. When the
entropy is large, it indicates the sample is close to the domain
classification boundary and we will relabel it to the source domain.

For unsupervised domain adaption classification, we denote the source domain
as $\mathcal{D}_{S}=\\{(\mathbf{x}_{i}^{s},y_{i}^{s})\\}_{i=1}^{N_{s}}$ with
$N_{s}$ labeled samples covering $C$ classes, and the target domain as
$\mathcal{D}_{T}=\\{\mathbf{x}_{j}^{t}\\}_{j=1}^{N_{t}}$ with $N_{t}$
unlabeled samples that belong to the same $C$ classes. UDA aims to train on
$\mathcal{D}_{S}$ and $\mathcal{D}_{T}$ to achieve high accuracy on the target
domain test set.

To be self-contained, we first review the adversarial domain adaptation
framework in Section 3.1. On top of the framework, we introduce our proposed
training strategy: Re-enforceable Adversarial Domain Adaptation (RADA) in
Section 3.2.

###  3.1 Recap of Adversarial Domain Adaptation

Adversarial Domain Adaptation aims to learn domain invariant representations
for UDA, by adversarially training the feature extractor and domain
discriminator/classifier. The adversarial domain adaptation [13, 30, 61, 10]
can be achieved by optimizing object classification loss $\mathcal{L}_{cls}$
and domain adversarial loss $\mathcal{L}_{adv}$ [12, 21, 30]:

$\displaystyle\mathcal{L}_{cls}=$
$\displaystyle\frac{1}{N_{s}}\sum_{i=1}^{N_{s}}\mathcal{L}_{ce}(C(F({x}_{i}^{s}))),y_{i}^{s}),$
(1) $\displaystyle\mathcal{L}_{adv}=$
$\displaystyle-\frac{1}{N_{s}}\sum_{i=1}^{N_{s}}\log D(F({x}_{i}^{s}))$
$\displaystyle-\frac{1}{N_{t}}\sum_{j=1}^{N_{t}}\log(1-D(F({x}_{j}^{t}))),$
(2)

where $F,C,D$ denote the feature extractor, the object classifier, and domain
discriminator/classifier, respectively. $\mathcal{L}_{ce}$ is the cross-
entropy loss. The total training objective is described as follows:

$\displaystyle\min_{D}\mathcal{L}_{adv},$ (3)
$\displaystyle\min_{F,C}\mathcal{L}_{cls}-\lambda\mathcal{L}_{adv},$ (4)

where the domain classifier $D$ is trained to discriminate the domains.
Conversely, the feature extractor is trained to extract features that make it
difficult for the domain classifier to discriminate the domains, resulting in
domain invariant features. $\lambda$ denotes a hyper-parameter for balancing
the losses. Gradient reversal layer (GRL) [12] which connects feature
extractor $F$ and the domain discriminator $D$ is usually used to achieve the
adversarial function by multiplying the gradient from $D$ by a certain
negative constant during the back-propagation to the feature extractor.

###  3.2 Proposed Training Strategy RADA

During the training, the discrimination capability of the domain classifier
w.r.t. to the increasingly aligned distributions _deteriorates_. This in turn
negatively influences the effectiveness of the optimization towards feature
alignment. Thus, with the adversarial optimization objectives as in (3) and
(4), the networks converge to states that are less than optimal. Even though
the distributions are not satisfactorily/fully aligned, the deteriorated
discrimination capability of the domain classifier cannot further efficiently
drive the feature alignment. We propose to re-energize the domain classifier
by making the two distributions more separable. As illustrated in Figure 3
(c), we relabel the “well aligned” (ambiguous w.r.t. domain classifier) target
domain samples as source domain samples. _Such relabeling makes the less
separable distributions more separable and thus leads to a more powerful
domain classifier, which in turn further drives feature alignment._ As
revealed in Figure 2 (a), the discrimination capability of the domain
discriminator after using our relabeling strategy is stronger (i.e., with
smaller entropy) than the baseline.

Measurement of Alignment for a Target Sample. To select and relabel the “well
aligned” target samples, we need to evaluate whether a target sample is “well
aligned” or not. As we know, when a sample is more aligned, it is harder for
the domain classifier to identify its domain and has a higher uncertainty
w.r.t. to the predicted domain label of this sample. Correspondingly, the
entropy of domain classification of this sample is in general higher.
Therefore, we measure how well a target sample is aligned with the source
domain by simply using the entropy of domain classification,

${\mathcal{H}}(\mathbf{p})=-p_{0}\log p_{0}-(1-p_{0})\log(1-p_{0}),$ (5)

where $\mathbf{p}$ = [$p_{0}$, $1-p_{0}$], with $p_{0}$ and $1-p_{0}$ denoting
the probability of predicting a sample as target domain and source domain,
respectively. The larger the entropy value, the larger the
ambiguity/uncertainty of the discriminator has when identifying which domain
it belongs to, and the more aligned the sample is. Note that there may exist
other more accurate or advanced measurement metrics, but this is not the focus
of this paper and we use this simple one here.

Well aligned Target Sample Selection and Relabeling. For a given target
sample, when it cannot be well distinguished by the domain classifier, it
could be considered approximately aligned. In other words, when the entropy of
the domain classifier prediction is larger than a threshould $\tau$, we define
it as a “well aligned” sample, which is to be relabeled as source domain.
$\tau$ is a hyper-parameter that controls the strictness of the definition of
“well align”. We will study its influence in the experiments in Section 4.

When to start RADA. Intuitively, we could start our relabeling strategy to re-
energize the domain discriminator whenever its discrimination capability
begins to deteriorate. In general, at the early training stage, the
optimization of the domain discriminator persistently improves its
discrimination capability as revealed in Figure 2 (a), where the entropy
decreases rapidly (in the first 5 epochs). It is thus unnecessary to enable
the re-energizing strategy. Inspired by the popular learning rate (lr)
adjustment algorithms [38, 3] which adjust the learning rate if no improvement
is seen for a ‘patience’ number of epochs (where ‘patience’ is usually set to
2 to 10)
111https://pytorch.org/docs/stable/optim.html?highlight=reducelronplateau
#torch.optim.lr_scheduler.ReduceLROnPlateau, we start RADA if no improvement
of the discrimination capability is seen for a ‘patience’ number of epochs and
we denote this hyper-parameter as $K$.

Adversarial Training. As is done in previous adversarial domain adaptation
methods, we perform mini-batch level optimization where a batch consists of
both source and target domain samples. Once RADA is activated, for each mini-
batch, we first check whether each target sample should be relabeled as a
source sample and relabel them if deemed so. Then the adversarial training is
performed under the updated domain labels. The domain classifier is updated by
minimizing the domain classification loss (i.e., adversarial loss as in (2))
_over the updated source set and target set_. Simultaneously, the feature
extractor is trained to fool the domain classifier. Note that the domain
relabeling has no impact on object classification loss.

Mixup within Updated Source Set. To promote the continuity of feature space in
the updated source set, where there may be low density space between the newly
absorbed samples and previous source samples, we leverage the mixup technique
[58] to softly mix the features between the previous source samples and the
newly absorbed samples. In a mini-batch, we randomly select a sample from the
original source set and a sample from the relabeled source sample set, where
we denote their features as $\mathbf{f}_{i}^{s}$ and $\mathbf{f}_{j}^{t}$,
respectively. Such mixup generates a new feature $\widetilde{\mathbf{f}^{s}}$
with domain label being source domain:

$\displaystyle\widetilde{\mathbf{f}^{s}}$
$\displaystyle=\mathcal{M}_{\alpha}(\mathbf{f}_{i}^{s},\mathbf{f}_{j}^{t})=\alpha\mathbf{f}_{i}^{s}+(1-\alpha)\mathbf{f}_{j}^{t},$
(6)

which is used to train the domain classifier and adversarially train the
feature extractor. $\alpha$ is a value randomly selected from a uniform
distribution, i.e., $\alpha\sim U(0,1)$.

##  4 Experiment

###  4.1 Datasets and Implementation Details

Datasets. 1) Office-31 [40] is the most widely used dataset for visual domain
adaptation (DA), with 4,652 images and 31 categories collected from three
distinct domains: Amazon (A), Webcam (W) and DSLR (D). We evaluate all methods
on six transfer tasks A $\rightarrow$ W, D $\rightarrow$ W, W $\rightarrow$ D,
A $\rightarrow$ D, D $\rightarrow$ A, and W $\rightarrow$ A, respectively. 2)
Office-Home [47] is a more difficult dataset (with relative large domain
discrepancy) than Office-31. It consists of 15,500 images of 65 object classes
in office and home settings. It has four dissimilar domains: Artistic images
(Ar), ClipArt (Cl), Product images (Pr), and Real-World images (Rw). Among the
four domains, there are a total of 12 DA tasks. 3) VisDA-2017 [36] is a
simulation-to-real dataset for DA with over 280,000 images across 12
categories in the training, validation and testing domains. 4) DomainNet [35]
is a benchmark for large-scale multi-source domain adaptation, which has six
domains (Clipart, Infograph, Painting, Quickdraw, Real and Sketch) and 0.6M
images of 345 classes. 5) Digit-Five consists of five different digit
recognition datasets: MNIST (mt) [27], MNIST-M (mm) [12], USPS (up) [23], SVHN
(sv) [33] , and Synthetic (syn) [12].

Following previous works [10, 55, 24], we perform single source to single
target adaptation on the first three datasets. We also conduct experiments for
multi-source domain adaptation on Digit-Five and DomainNet, following the
leave-one-out protocol used in works [32, 35], where one domain is selected as
the target domain while the rest of domains are used as source domains.

Implementation Details. _Baseline Setup:_ We build our baseline networks based
on two representative domain adversarial domain adaptation frameworks: DANN
[13] and CDAN [30]. Following [30], we could also use entropy conditioning (E)
regularization as proposed in CDAN [30], which reweighs the samples based on
their entropy of the object classification predictions to prioritize the easy-
to-transfer samples for easy optimization. We denote the schemes with entropy
conditioning (E) regularization as CDAN+E, DANN+E. We employ stochastic
gradient descent (SGD) as optimizer with an initial learning rate of 1e-3 and
momentum of 0.9 to train all the models.

_Our RADA Setup:_ As a plug-and-play optimization strategy, we apply our RADA
on top of the above two representative UDA baselines (DANN and CDAN) for
validation. Batch size is set as 36 for Office-31 and VisDA-2017. More details
are presented in Supplementary. As discussed in Section 3.2, we perform the
“well aligned” target sample selection, relabeling, and domain discriminator
and feature extractor updating with the updated domain partitions at the mini-
batch level. We train our RADA method for 100 epochs on Office-31 and Office-
Home, and 150 epochs for the larger VisDA-2017 dataset. We set threshould
$\tau$ to 0.35 and $K$ to 5 by default, which we have investigated their
influences in our ablation study section.

###  4.2 Ablation Study

We perform comprehensive ablation studies to demonstrate the effectiveness of
our proposed Re-enforceable Adversarial Domain Adaptation strategy. We conduct
experiments on the two representative adversarial domain adaptation frameworks
_DANN_ [13] and _CDAN_ [30].

Table 1: Performance (classification accuracy %) comparisons of our schemes and the corresponding baselines. “w/o MU” denotes without using mixup within the updated source set. All schemes use ResNet-50 as backbone.  Methods | Office-31 | Office-Home | VisDA-2017
---|---|---|---
Baseline (DANN [13]) | 83.42 | 60.05 | 61.23
DANN+RADA w/o MU | 85.24 | 63.14 | 65.91
DANN+RADA | 86.79 | 64.81 | 67.29
Baseline (CDAN [30]) | 87.90 | 68.11 | 70.82
CDAN+RADA w/o MU | 89.58 | 70.25 | 75.62
CDAN+RADA | 91.08 | 71.37 | 76.28

Effectiveness of RADA. Table 1 shows the averaged domain adaptive
classification results on three benchmarks. We have the following
observations:

1) On top of the baseline _DANN_ , our _DANN+RADA w/o MU_ even _without using
mixup_ (for the updated source set) brings significant improvement of
1.82%/3.09%/4.68% in accuracy on Office-31/Office-Home/VisDA-2017,
respectively. On top of the stronger baseline _CDAN_ , our _CDAN+RADA w/o MU_
still brings significant improvements, i.e., 1.68%/2.14%/4.80% in accuracy on
Office-31/Office-Home/VisDA-2017, respectively.

2) Mixup within the updated source set promotes the continuity in the updated
source set. It brings additional gain of 1.55%/1.67%/1.38% in accuracy on
Office-31/Office-Home/VisDA-2017 on top of _DANN+RADA w/o MU_. Note that on
VisDA-2017, the majority of gain comes from our relabeling strategy, i.e.,
4.68%/4.80% on DANN/CDAN, whereas the mixup specific to our updated source set
brings additional gain of 1.38%/0.66%.

3) The performance improvement of our _RADA_ over _Baseline_ on the large
dataset VisDA-2017 is larger than that on the other two datasets, which
reveals that our method is effective on the challenging dataset with large
domain gap (synthetic$\rightarrow$real).

Table 2: Comparison with re-weighting related methods for multi-source domain adaptation on digits classification.  Methods | Digit-Five
---|---
mt | mm | sv | sy | up | Avg.
DANN [11] | 97.9 | 70.8 | 68.5 | 87.3 | 93.4 | 83.6
DANN + E [30] | 98.4 | 73.7 | 71.5 | 87.8 | 94.7 | 85.2
DANN + inverse-E [30] | 98.7 | 74.4 | 72.4 | 88.6 | 96.4 | 86.1
DANN+IWAN [59] | 98.2 | 74.2 | 72.9 | 88.9 | 95.8 | 86.0
DANN+RADA w/o MU | 99.0 | 76.6 | 88.9 | 94.7 | 98.4 | 91.5
DANN+RADA w/ MU | 99.3 | 78.2 | 90.0 | 95.2 | 98.4 | 92.2

Comparison with Sample Re-weighting based Methods. We compare RADA with some
sample (re)weighting based schemes, including entropy-based re-weighting (E)
[30], IWAN [59]. Entropy-based re-weighting (E) aims to prioritize the easy-
to-transfer samples to ease the optimization, sharing a similar idea in
curriculum learning. IWAN [59] re-weights the samples based on the outputs of
domain discriminator, which assigns small weights to those source samples that
are easy to be distinguished by the domain discriminator to exclude the
outlier classes in the source domain. _DANN+inverse-E_ denotes the scheme that
after half of the optimization iterations, we turn the focus (large weight)
from easy-to-transfer samples to those hard-to-aligned samples to enable hard-
mining, by adjusting entropy-based reweighting strategy from
$\omega(ent(\cdot))=1+e^{-ent(\cdot)}$ to
$\omega(ent(\cdot))=1/(1+e^{-ent(\cdot)})$, where $ent(\cdot)$ denotes the
entropy of the predicted object classes.

We conduct experiments under the multi-source setup on Digit-Five. To
guarantee fairness of comparison, all competitors use the same network
architecture (the feature extractor is composed of three convolutional layers
and two FC layers ($Cov_{3}FC_{2}$)), and are built upon the representative
domain adversarial training framework DANN [13].

Table 2 shows the results. We can see that all sample re-weighting strategies
bring performance gain. Our RADA strategy outperforms the performance of all
these strategies. In essence, the re-weighting based methods adjust the levels
of importance of samples during optimization (from the perspective of
curriculum learning or hard mining). They all use static domain labels in
optimization. Giving larger weights to the “well aligned” samples actually
provides more aligned distributions to the domain discriminator, which cannot
effectively enhance the discrimination capability of the domain classifier.
Giving smaller weights to the “well aligned” samples may result in less
effective use of them for training the domain discriminator. _In contrast, we
allow dynamic domain labels by relabeling those “well aligned” target domain
samples as source domain samples. On one hand, this provides more separable
distributions (vs. more aligned distributions as in assigning larger weights)
to re-energize the domain discriminator. On the other hand, this enables the
full use of “well aligned” samples (vs. less effective use as in assigning
smaller weights)._

###  4.3 Design Choices

For clear analysis, we do experiments on our scheme _RADA w/o MU_ (i.e.,
without using mixup for the updated source set) for design choices study on
VisDA-2017.

Influence of Threshold $\tau$. As described in Section 3.2, we employ a hyper-
parameter $\tau$ as the threshold to determine whether a target sample is
“well aligned”. We study its influence in Figure 4 (a). We can see that a
superior performance is achieved when $\tau$ ranges from 0.3 to 0.35 on two
schemes. Similar trends are observed on other datasets. We set $\tau=0.35$ on
all datasets. Note that we set $K=5$ for all these comparison experiments.

Figure 4: Influence of (a) threshold $\tau$, and (b) $K$.

Influence of $K$. As described in Section 3.2, we start our RADA if no
improvement of the discrimination capability of the domain classifier is seen
for $K$ number of epochs. Figure 4 (b) shows the influence of $K$. We observe
that superior performance is achieved when $K$ ranges from 4 to 6. Similar
trends are observed on other datasets. We set $K=5$ for all datasets. When $K$
is too small, the judgement of no improvement is not reliable which is
sensitive to noise. When $K$ is too large, this optimization strategy cannot
be fully utilized.

Figure 5: Visualization of t-SNE distributions on Digit-Five. Compared with CDAN, the features of different domains are better aligned and the different classes/digits are clearly separated for our CDAN+RADA. Table 3: Performance (%) comparisons with the state-of-the-art UDA approaches on Office-31. All experiments are conducted based on ResNet-50 pre-trained on ImageNet. Method | Venue |  A$\rightarrow$D |  A$\rightarrow$W |  D$\rightarrow$W |  W$\rightarrow$D |  D$\rightarrow$A |  W$\rightarrow$A | Avg.
---|---|---|---|---|---|---|---|---
DANN [13] | JMLR’16 |  79.7$\pm$0.4 |  82.0$\pm$0.4 |  96.9$\pm$0.2 |  99.1$\pm$0.1 |  68.2$\pm$0.4 |  67.4$\pm$0.5 | 82.2
Simnet [37] | CVPR’18 |  85.3$\pm$0.3 |  88.6$\pm$0.5 |  98.2$\pm$0.2 |  99.7$\pm$0.2 |  73.4$\pm$0.8 |  71.8$\pm$0.6 | 86.2
MCD [41] | CVPR’18 |  92.2$\pm$0.2 |  88.6$\pm$0.2 |  98.5$\pm$0.1 | 100.0$\pm$.0 |  69.5$\pm$0.1 |  69.7$\pm$0.3 | 86.5
CDAN [30] | NIPS’18 |  92.9$\pm$0.2 |  93.1$\pm$0.1 |  98.6$\pm$0.1 | 100.0$\pm$.0 |  71.0$\pm$0.3 |  69.3$\pm$0.3 | 87.5
TADA [49] | AAAI’19 |  91.6$\pm$0.3 |  94.3$\pm$0.3 |  98.7$\pm$0.1 |  99.8$\pm$0.2 |  72.9$\pm$0.2 |  73.0$\pm$0.3 | 88.4
Symnets [61] | CVPR’19 |  93.9$\pm$0.5 |  90.8$\pm$0.1 |  98.8$\pm$0.3 | 100.0$\pm$.0 |  74.6$\pm$0.6 |  72.5$\pm$0.5 | 88.4
CAN [26] | CVPR’19 |  95.0$\pm$0.3 |  94.5$\pm$0.3 |  99.1$\pm$0.2 |  99.8$\pm$0.2 | 78.0$\pm$0.3 |  77.0$\pm$0.3 | 90.6
GVB [10] | CVPR’20 |  95.0$\pm$0.4 |  94.8$\pm$0.5 |  98.7$\pm$0.3 | 100.0$\pm$.0 |  73.4$\pm$0.3 |  73.7$\pm$0.4 | 89.3
CDAN-GVB [10] | CVPR’20 |  93.7$\pm$0.2 |  94.0$\pm$0.2 |  98.6$\pm$0.1 | 100.0$\pm$.0 |  73.4$\pm$0.3 |  73.0$\pm$0.2 | 88.8
SRDC [44] | CVPR’20 |  95.8$\pm$0.2 |  95.7$\pm$0.2 |  99.2$\pm$0.1 | 100.0$\pm$.0 |  76.7$\pm$0.3 |  77.1$\pm$0.1 | 90.8
CDAN (Baseline) [30] | NIPS’18 |  90.8$\pm$0.3 |  94.0$\pm$0.5 |  98.1$\pm$0.3 | 100.0$\pm$.0 |  72.4$\pm$0.4 |  72.1$\pm$0.3 | 87.9
CDAN+RADA | This work | 96.1$\pm$0.4 | 96.2$\pm$0.4 | 99.3$\pm$0.1 | 100.0$\pm$.0 |  77.5$\pm$0.1 | 77.4$\pm$0.3 | 91.1
Table 4: Performance (%) comparisons with the state-of-the-art UDA approaches
on Office-Home, VisDA-2017, Digit-Five, and DomainNet. For all these
approaches, ResNet-50 is taken as backbone for Office-Home, VisDA-2017,
$Cov_{3}FC_{2}$ is taken as backbone for Digit-Five, ResNet-101 is taken as
backbone for DomainNet. See Supplementary for more results.

Method | Venue | Avg.
---|---|---
DANN [13] | JMLR’16 | 57.6
MCD [41] | CVPR’18 | 64.1
CDAN [30] | NIPS’18 | 65.8
Symnets [61] | CVPR’19 | 67.2
TADA [49] | AAAI’19 | 67.6
BNM [9] | CVPR’20 | 67.9
CDAN-GVB [10] | CVPR’20 | 69.0
GVB [10] | CVPR’20 | 70.4
SRDC [44] | CVPR’20 | 71.3
CDAN (Baseline) | NIPS’18 | 68.1
CDAN+RADA | This work | 71.4
(a) Comparison on Office-Home.

Method | Venue | Avg.
---|---|---
DAN [29] | ICML’15 | 61.6
DANN [13] | JMLR’16 | 57.4
GTA [42] | CVPR’18 | 69.5
CDAN [30] | NIPS’18 | 70.0
MDD [60] | ICML’19 | 74.6
CDAN-GVB [10] | CVPR’20 | 74.9
GVB [10] | CVPR’20 | 75.3
CDAN (Baseline) | NIPS’18 | 70.8
CDAN+RADA | This work | 76.3
(b) Comparison on VisDA-2017.

Methods | Venue | Avg.
---|---|---
DANN [13] | JMLR’16 | 83.6
IWAN [56] | ICML’19 | 86.0
MDAN [62] | NIPS’18 | 86.7
MCD [41] | CVPR’18 | 86.1
DCTN [53] | CVPR’18 | 88.6
M3SDA [35] | ICCV’19 | 87.6
CMSS [55] | ECCV’20 | 90.8
CDAN (Baseline) | NIPS’18 | 88.7
CDAN+RADA | This work | 93.2
(c) Comparison on Digit-Five.

Methods | Venue | Avg.
---|---|---
DANN [13] | JMLR’16 | 32.7
DCTN [53] | CVPR’18 | 38.3
MCD [41] | CVPR’18 | 38.5
MDAN [62] | NIPS’18 | 42.8
M3SDA [35] | ICCV’19 | 42.7
FAR [24] | Arxiv’19 | 45.5
CMSS [55] | ECCV’20 | 46.5
CDAN (Baseline) | NIPS’18 | 45.2
CDAN+RADA | This work | 47.5
(d) Comparison on DomainNet.

###  4.4 Visualization

Visualization of Feature Distributions. In Figure 5, we visualize the
distributions of the features using t-SNE [46] in _mm,mt,sv,syn
$\rightarrow$up_ setting on Digit-Five. We compare the feature distribution
with the baseline CDAN [30], and observe that the features of different
domains are better aligned and the different classes/digits are clearly
separated for our scheme CDAN+RADA.

###  4.5 Comparison with State-of-the-Arts

We compare our scheme CDAN [30]+RADA with state-of-the-art unsupervised
single/multi-source domain adaptation methods. We report the results from
their original papers if available. For fair comparison, we also report the
result of our baseline scheme CDAN run by us. SRDC [44], CAN [26], and Symnets
[61] tend to encourage the category/group-level consistency of distributions
to alleviate the damage of intrinsic discrimination of data when minimizing
the domain discrepancy. GVB [10] and CMSS [55] explore to reduce domain
discrepancy in a curriculum manner. Ours is conceptually complementary to
these methods. They in general ignore the degradation of the discrimination
capability of the domain discriminator and its side effect. To address this,
we propose RADA which could re-energize the domain discriminator by exploring
dynamic domain labels, without making any change to the network architecture
of the domain discriminator.

Results on Office-31, Office-Home and VisDA-2017. For the single source to
single target adaptation, Table 3, Table 4a and Table 4b show the comparisons
on Office-31, Office-Home, and VisDA-2017. We can see that our CDAN+RADA
significantly outperforms the baseline CDAN [30] by 3.2%, 3.3%, and 5.5% on
three datasets. CDAN+RADA also achieves the best performance.

Results on Digit-Five and DomainNet. We also conduct experiments on the multi-
source to single target adaptation settings on Digit-Five and DomainNet. On
Table 4c and Table 4d show that our CDAN+RADA outpeforms our baseline CDAN by
4.5% and 2.3% on Digit-Five and DomainNet, respectively. Our CDAN+RADA also
achieves the state-of-the-art performance. More experimental results,
including various sub-settings on different single/multi-source UDA datasets,
can be found in Supplementary.

##  5 Conclusion

In this paper, we propose an effective optimization strategy for adversarial
domain adaptation, termed as Re-enforceable Adversarial Domain Adaptation
(RADA), which aims to re-energize the domain classifier during the training
and in turn further drives feature alignment. We achieve this by relabeling
the “well aligned” target samples as source domain, which enables the
exploration of dynamic domain labels, for re-energizing the domain classifier
on the fly. Extensive experiments on multiple benchmarks, including single-
and multi-source domain adaptation, and different adversarial domain
adaptation networks, have demonstrated the effectiveness and generalization
capability of our RADA strategy for adversarial domain adaptation. We expect
this work to inspire more works which investigate/incorporate more dynamics to
the adversarial domain adaption for effective training.

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