Original: A DVERSARIAL D ROPOUT R EGULARIZATION
Revised: A DVERSARIAL D ROPOUT R EGULARIZATION

Original: Anonymous authors
Revised: Anonymous authors

Original: Paper under double-blind review
Revised: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: We present a method for transferring neural representations from label-rich source domains to unlabeled target domains.
Revised: We present a method for transferring neural representations from label-rich source domains to unlabeled target domains.

Original: Recent adversarial methods proposed for this task learn to align features across domains by fooling a special domain critic network.
Revised: Recent adversarial methods proposed for this task learn to align features across domains by fooling a special domain critic network.

Original: However, a drawback of this approach is that the critic simply labels the generated features as in-domain or not, without considering the boundaries between classes.
Revised: However, a drawback of this approach is that the critic simply labels the generated features as in-domain or not, without considering the boundaries between classes.

Original: This can lead to ambiguous features being generated near class boundaries, reducing target classiﬁcation accuracy.
Revised: This can lead to ambiguous features being generated near class boundaries, reducing target classiﬁcation accuracy.

Original: We propose a novel approach,
Revised: We propose a novel approach,

Original: Adversarial Dropout Regularization (ADR), to encourage the generator to output more discriminative features for the target domain.
Revised: Adversarial Dropout Regularization (ADR), to encourage the generator to output more discriminative features for the target domain.

Original: Our key idea is to replace the critic with one that detects non-discriminative features, using dropout on the classiﬁer network.
Revised: Our key idea is to replace the critic with one that detects non-discriminative features, using dropout on the classiﬁer network.

Original: The generator then learns to avoid these areas of the feature space and thus creates better features.
Revised: The generator then learns to avoid these areas of the feature space and thus creates better features.

Original: We apply our ADR approach to the problem of unsupervised domain adaptation for image classiﬁcation and semantic segmentation tasks, and demonstrate signiﬁcant improvement over the state of the art.
Revised: We apply our ADR approach to the problem of unsupervised domain adaptation for image classiﬁcation and semantic segmentation tasks, and demonstrate signiﬁcant improvement over the state of the art.

Original: We also show that our approach can be used to train Generative Adversarial Networks for semi-supervised learning.
Revised: We also show that our approach can be used to train Generative Adversarial Networks for semi-supervised learning.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Transferring knowledge learned by deep neural networks on label-rich domains to new target domains is a challenging problem, especially when the source and target input distributions have different characteristics.
Revised: Transferring knowledge learned by deep neural networks on label-rich domains to new target domains is a challenging problem, especially when the source and target input distributions have different characteristics.

Original: For example, while simulated driving images rendered by games provide a rich source of labeled data for semantic segmentation, deep models trained on such source data do not transfer well to real target domains (Fig.
Revised: For example, while simulated driving images rendered by games provide a rich source of labeled data for semantic segmentation, deep models trained on such source data do not transfer well to real target domains (Fig.

Original: 1 (a-d)).
Revised: 1 (a-d)).

Original: When target-domain labels are unavailable for ﬁne-tuning, unsupervised domain adaptation must be applied to improve the source model.
Revised: When target-domain labels are unavailable for ﬁne-tuning, unsupervised domain adaptation must be applied to improve the source model.

Original: Recent methods for unsupervised domain adaptation attempt to reduce the discrepancy between the source and target features via adversarial learning (Tzeng et al.
Revised: Recent methods for unsupervised domain adaptation attempt to reduce the discrepancy between the source and target features via adversarial learning (Tzeng et al.

Original: ; Ganin & Lempitsky (2014)).
Revised: ; Ganin & Lempitsky (2014)).

Original: They divide the base network into a feature encoder G and classiﬁer C , and add a separate domain classiﬁer (critic) network D .
Revised: They divide the base network into a feature encoder G and classiﬁer C , and add a separate domain classiﬁer (critic) network D .

Original: The critic takes the features generated by G and labels them as either source- or target-domain.
Revised: The critic takes the features generated by G and labels them as either source- or target-domain.

Original: The encoder E is then trained with an additional adversarial loss that maximizes D ’s mistakes and thus aligns features across domains.
Revised: The encoder E is then trained with an additional adversarial loss that maximizes D ’s mistakes and thus aligns features across domains.

Original: However, a major drawback of this approach is that the critic simply predicts the domain label of the generated point and does not consider category information.
Revised: However, a major drawback of this approach is that the critic simply predicts the domain label of the generated point and does not consider category information.

Original: Thus the generator may create features that look like they came from the right domain, but are not discriminative.
Revised: Thus the generator may create features that look like they came from the right domain, but are not discriminative.

Original: In particular, it can generate points close to class boundaries, as shown in Fig. 1(e), which are likely to be mis-classiﬁed by the source model.
Revised: In particular, it can generate points close to class boundaries, as shown in Fig. 1(e), which are likely to be mis-classiﬁed by the source model.

Original: We argue that to achieve good performance on the target data, the adaptation model must take the decision boundaries between classes into account while aligning features across domains (Fig. 1(f)).
Revised: We argue that to achieve good performance on the target data, the adaptation model must take the decision boundaries between classes into account while aligning features across domains (Fig. 1(f)).

Original: The problem is, it is not clear how this can be accomplished without labels on target data.
Revised: The problem is, it is not clear how this can be accomplished without labels on target data.

Original: In this paper, we propose a novel adversarial alignment technique that overcomes the above limitation and preserves class boundaries.
Revised: In this paper, we propose a novel adversarial alignment technique that overcomes the above limitation and preserves class boundaries.

Original: We make the following observation: if the critic could detect points near the decision boundary, then the generator would have to avoid these areas of the feature space in order to fool the critic.
Revised: We make the following observation: if the critic could detect points near the decision boundary, then the generator would have to avoid these areas of the feature space in order to fool the critic.

Original: Thus the critic would force the generator to create more discriminative features.
Revised: Thus the critic would force the generator to create more discriminative features.

Original: How can we obtain such a critic?
Revised: How can we obtain such a critic?

Original: If we alter the boundary of the classiﬁer C slightly and measure the change in the posterior class probability p ( y | x ) , where y and x denote class and input respectively, then samples near the decision boundary are likely to have the largest change.
Revised: If we alter the boundary of the classiﬁer C slightly and measure the change in the posterior class probability p ( y | x ) , where y and x denote class and input respectively, then samples near the decision boundary are likely to have the largest change.

Original: In fact, this posterior discrepancy is inversely proportional to the distance from the class boundary.
Revised: In fact, this posterior discrepancy is inversely proportional to the distance from the class boundary.

Original: [Figure]
Revised: [Figure]

Original: We thus propose to maximize this posterior discrepancy to turn C into a critic sensitive to nondiscriminative points.
Revised: We thus propose to maximize this posterior discrepancy to turn C into a critic sensitive to nondiscriminative points.

Original: We call this technique Adversarial Dropout Regularization .
Revised: We call this technique Adversarial Dropout Regularization .

Original: Here, dropout is not used in the standard way, which is to regularize the main classiﬁer and make it insensitive to noise.
Revised: Here, dropout is not used in the standard way, which is to regularize the main classiﬁer and make it insensitive to noise.

Original: Instead, we use dropout in an adversarial way, to transform the classiﬁer into a critic sensitive to noise.
Revised: Instead, we use dropout in an adversarial way, to transform the classiﬁer into a critic sensitive to noise.

Original: Compared to previous adversarial feature alignment methods, where the distributions p ( x ) are aligned globally, our method aligns target features away from decision boundaries, as illustrated in Fig.1(f).
Revised: Compared to previous adversarial feature alignment methods, where the distributions p ( x ) are aligned globally, our method aligns target features away from decision boundaries, as illustrated in Fig.1(f).

Original: Our ADR approach has several beneﬁts.
Revised: Our ADR approach has several beneﬁts.

Original: First, we train the generator G with feedback from the classiﬁer C , in contrast to existing methods, which use an unrelated critic D .
Revised: First, we train the generator G with feedback from the classiﬁer C , in contrast to existing methods, which use an unrelated critic D .

Original: Second, our method is general and straightforward to apply to a variety of domain adaptation problems, such as classiﬁcation and semantic segmentation.
Revised: Second, our method is general and straightforward to apply to a variety of domain adaptation problems, such as classiﬁcation and semantic segmentation.

Original: Finally, since ADR is trained to align distributions, it is also applicable to semi-supervised learning and training of generative models, such as Generative Adversarial
Revised: Finally, since ADR is trained to align distributions, it is also applicable to semi-supervised learning and training of generative models, such as Generative Adversarial

Original: Networks (GANs) (Goodfellow et al.
Revised: Networks (GANs) (Goodfellow et al.

Original: (2014a)).
Revised: (2014a)).

Original: Through extensive experiments, we demonstrate the beneﬁt of ADR over existing domain adaptation approaches, achieving state-of-the-art results in difﬁcult domain shifts.
Revised: Through extensive experiments, we demonstrate the beneﬁt of ADR over existing domain adaptation approaches, achieving state-of-the-art results in difﬁcult domain shifts.

Original: We also show an application to semi-supervised learning using GANs.
Revised: We also show an application to semi-supervised learning using GANs.

Original:  R ELATED W ORK
Revised:  R ELATED W ORK

Original: Domain Adaptation.
Revised: Domain Adaptation.

Original: Recent unsupervised domain adaptation (UDA) methods for visual data aim to align the feature distributions of the source and target domains (Sun et al.
Revised: Recent unsupervised domain adaptation (UDA) methods for visual data aim to align the feature distributions of the source and target domains (Sun et al.

Original: ; Sun & Saenko (2016);
Revised: ; Sun & Saenko (2016);

Original: Tzeng et al.
Revised: Tzeng et al.

Original: ; Ganin et al.
Revised: ; Ganin et al.

Original: ; Long et al. (2015b); Yan et al.
Revised: ; Long et al. (2015b); Yan et al.

Original: Long et al.
Revised: Long et al.

Original: Such methods are motivated by theoretical results stating that minimizing the divergence between domains will lower the upper bound of the error on target domain (Ben-David et al.
Revised: Such methods are motivated by theoretical results stating that minimizing the divergence between domains will lower the upper bound of the error on target domain (Ben-David et al.

Original: Many works in deep learning utilize the technique of distribution matching in hidden layers of a network such as a CNN (Tzeng et al.
Revised: Many works in deep learning utilize the technique of distribution matching in hidden layers of a network such as a CNN (Tzeng et al.

Original: ; Ganin et al.
Revised: ; Ganin et al.

Original: ; Long et al. (2015b)).
Revised: ; Long et al. (2015b)).

Original: However, they measure the domain divergence based on the hidden features of the network without considering the relationship between its decision boundary and the target features, as we do.
Revised: However, they measure the domain divergence based on the hidden features of the network without considering the relationship between its decision boundary and the target features, as we do.

Original: Low-density Separation.
Revised: Low-density Separation.

Original: Many semi-supervised learning (SSL) methods utilize the relationship between the decision boundary and unlabeled samples, a technique called low-density separation (Chapelle & Zien (2005); Joachims (1999)).
Revised: Many semi-supervised learning (SSL) methods utilize the relationship between the decision boundary and unlabeled samples, a technique called low-density separation (Chapelle & Zien (2005); Joachims (1999)).

Original: By placing the boundary in the area where the unlabeled samples are sparse, these models aim to obtain discriminative representations.
Revised: By placing the boundary in the area where the unlabeled samples are sparse, these models aim to obtain discriminative representations.

Original: Our method aims to achieve low-density separation for deep domain adaptation and is related to entropy minimization for semi-supervised learning (Grandvalet & Bengio (2005)).
Revised: Our method aims to achieve low-density separation for deep domain adaptation and is related to entropy minimization for semi-supervised learning (Grandvalet & Bengio (2005)).

Original: (Long et al.
Revised: (Long et al.

Original: (2016)) used entropy minimization in their approach to directly measure how far samples are from a decision boundary by calculating entropy of the classiﬁer’s output.
Revised: (2016)) used entropy minimization in their approach to directly measure how far samples are from a decision boundary by calculating entropy of the classiﬁer’s output.

Original: On the other hand, our method tries to achieve low-density separation by slightly moving the boundary and detecting target samples sensitive to the movement.
Revised: On the other hand, our method tries to achieve low-density separation by slightly moving the boundary and detecting target samples sensitive to the movement.

Original: As long as target samples features are robust to the movement, they will be allowed to exist relatively nearby the boundary compared to source samples, as Fig.
Revised: As long as target samples features are robust to the movement, they will be allowed to exist relatively nearby the boundary compared to source samples, as Fig.

Original: 1 shows.
Revised: 1 shows.

Original: In (Long et al.
Revised: In (Long et al.

Original: entropy minimization is just a part of the overall approach.
Revised: entropy minimization is just a part of the overall approach.

Original: To compare our
Revised: To compare our

Original: ADR approach to entropy minimization directly, we use a new baseline method.
Revised: ADR approach to entropy minimization directly, we use a new baseline method.

Original: To our knowledge, though this method has not been proposed by any previous works, it is easily achieved by modifying a method proposed by (Springenberg (2015)).
Revised: To our knowledge, though this method has not been proposed by any previous works, it is easily achieved by modifying a method proposed by (Springenberg (2015)).

Original: For this baseline, we trained a model that generates features to minimize the entropy of the output probability for target samples.
Revised: For this baseline, we trained a model that generates features to minimize the entropy of the output probability for target samples.

Original: The detail of the baseline is written in our supplemental material.
Revised: The detail of the baseline is written in our supplemental material.

Original: The generator tries to minimize the entropy of the target samples whereas the critic tries to maximize it.
Revised: The generator tries to minimize the entropy of the target samples whereas the critic tries to maximize it.

Original: The entropy is directly measured by the output of the classiﬁer.
Revised: The entropy is directly measured by the output of the classiﬁer.

Original: This baseline is similar to our approach in that the goal of the method is to achieve low-density separation.
Revised: This baseline is similar to our approach in that the goal of the method is to achieve low-density separation.

Original: Dropout.
Revised: Dropout.

Original: Dropout is a method that prevents the networks from overﬁtting (Srivastava et al.
Revised: Dropout is a method that prevents the networks from overﬁtting (Srivastava et al.

Original: by randomly dropping units from the neural network during training.
Revised: by randomly dropping units from the neural network during training.

Original: Effectively, dropout samples from an exponential number of different thinned networks at training time, which prevents units from co-adapting too much.
Revised: Effectively, dropout samples from an exponential number of different thinned networks at training time, which prevents units from co-adapting too much.

Original: At test time, predictions are obtained by using the outputs of all neurons.
Revised: At test time, predictions are obtained by using the outputs of all neurons.

Original: If the thinned networks are able to classify the samples accurately, the full network will as well.
Revised: If the thinned networks are able to classify the samples accurately, the full network will as well.

Original: In other words, dropout encourages the network to be robust to noise.
Revised: In other words, dropout encourages the network to be robust to noise.

Original: In our work, we use dropout to regularize the feature generation network G , but in an adversarial way.
Revised: In our work, we use dropout to regularize the feature generation network G , but in an adversarial way.

Original: We train the critic C to be sensitive to the noise caused by dropout and use C to regularize G so that it generates noise-robust features.
Revised: We train the critic C to be sensitive to the noise caused by dropout and use C to regularize G so that it generates noise-robust features.

Original: To our knowledge, this use of dropout is completely different from existing methods.
Revised: To our knowledge, this use of dropout is completely different from existing methods.

Original:  M ETHOD
Revised:  M ETHOD

Original: We assume that we have access to a labeled source image x s and a corresponding label y s drawn from a set of labeled source images { X s , Y s } , as well as
Revised: We assume that we have access to a labeled source image x s and a corresponding label y s drawn from a set of labeled source images { X s , Y s } , as well as

Original: an unlabeled target image x t drawn from unlabeled target images X t .
Revised: an unlabeled target image x t drawn from unlabeled target images X t .

Original: We train a feature generation network G , which takes inputs x s or x t , and a network C that acts as both the main classiﬁer and the critic.
Revised: We train a feature generation network G , which takes inputs x s or x t , and a network C that acts as both the main classiﬁer and the critic.

Original: When acting as the classiﬁer, C takes features from G and classiﬁes them into K classes, predicting a K -dimensional vector of logits { l 1 , l 2 , l 3 ...l K } .
Revised: When acting as the classiﬁer, C takes features from G and classiﬁes them into K classes, predicting a K -dimensional vector of logits { l 1 , l 2 , l 3 ...l K } .

Original: The logits are then converted to class probabilities by applying the softmax function.
Revised: The logits are then converted to class probabilities by applying the softmax function.

Original: Namely, the probability that x is classiﬁed into class j is denoted by p ( y = j | x ) =
Revised: Namely, the probability that x is classiﬁed into class j is denoted by p ( y = j | x ) =

Original: exp ( l j ) (cid:80) Kk =1 exp ( l k ) .
Revised: exp ( l j ) (cid:80) Kk =1 exp ( l k ) .

Original: We use the notation p ( y | x ) to denote the K -dimensional probabilistic output for input x .
Revised: We use the notation p ( y | x ) to denote the K -dimensional probabilistic output for input x .

Original: When C is acting as the critic, we want it to detect the feature encodings of target samples near the decision boundary.
Revised: When C is acting as the critic, we want it to detect the feature encodings of target samples near the decision boundary.

Original: We propose to make C sensitive to such samples by slightly perturbing its decision boundary and measuring the change in the posterior class probability p ( y | x ) .
Revised: We propose to make C sensitive to such samples by slightly perturbing its decision boundary and measuring the change in the posterior class probability p ( y | x ) .

Original: This change is likely to be largest for samples near the decision boundary.
Revised: This change is likely to be largest for samples near the decision boundary.

Original: The network C is then trained to increase this change, while the feature generation network G is trained to decrease it.
Revised: The network C is then trained to increase this change, while the feature generation network G is trained to decrease it.

Original: Through this adversarial training, G learns to ‘fool’ the critic and generate target features far away from the decision boundary, thus avoiding ambiguous features.
Revised: Through this adversarial training, G learns to ‘fool’ the critic and generate target features far away from the decision boundary, thus avoiding ambiguous features.

Original: The weights of G can be initialized either by pre-training on some auxiliary dataset (e.g., Imagenet), or with random weights, while C uses random initialization.
Revised: The weights of G can be initialized either by pre-training on some auxiliary dataset (e.g., Imagenet), or with random weights, while C uses random initialization.

Original: In the next section, we show how we utilize dropout to perturb the boundary in the critic and measure sensitivity.
Revised: In the next section, we show how we utilize dropout to perturb the boundary in the critic and measure sensitivity.

Original: We then show the training procedure of our method.
Revised: We then show the training procedure of our method.

Original: Finally, we give some intuition behind adversarial dropout and improve our method based on this insight.
Revised: Finally, we give some intuition behind adversarial dropout and improve our method based on this insight.

Original: As the training procedure used for semisupervised learning with GANs is slightly different from the one used in domain adaptation experiments, we describe it in the corresponding experimental section.
Revised: As the training procedure used for semisupervised learning with GANs is slightly different from the one used in domain adaptation experiments, we describe it in the corresponding experimental section.

Original: 3. C LASSIFIER S ELECTION VIA D ROPOUT
Revised: 3. C LASSIFIER S ELECTION VIA D ROPOUT

Original: Consider the standard training of a neural network using dropout.
Revised: Consider the standard training of a neural network using dropout.

Original: For every sample within a minibatch, each node of the network is removed with some probability, effectively selecting a different classiﬁer for every sample during training.
Revised: For every sample within a minibatch, each node of the network is removed with some probability, effectively selecting a different classiﬁer for every sample during training.

Original: We harness this idea in a very simple way.
Revised: We harness this idea in a very simple way.

Original: We forward input features G ( x t ) to C twice, dropping different nodes each time and obtaining two different output vectors denoted as C 1 ( G ( x t )) , C 2 ( G ( x t )) .
Revised: We forward input features G ( x t ) to C twice, dropping different nodes each time and obtaining two different output vectors denoted as C 1 ( G ( x t )) , C 2 ( G ( x t )) .

Original: In other words, we are selecting two different classiﬁers C 1 and C 2 from C by dropout as in Fig.
Revised: In other words, we are selecting two different classiﬁers C 1 and C 2 from C by dropout as in Fig.

Original: In the ﬁgure, the corresponding posterior probabilities are indicated as p 1 ( y | x t ) , p 2 ( y | x t ) , abbreviated as p 1 and p 2 in the following discussion.
Revised: In the ﬁgure, the corresponding posterior probabilities are indicated as p 1 ( y | x t ) , p 2 ( y | x t ) , abbreviated as p 1 and p 2 in the following discussion.

Original: In order to detect the change of predictions near the boundary, the critic tries to increase the difference between the predictions of C 1 and C 2 .
Revised: In order to detect the change of predictions near the boundary, the critic tries to increase the difference between the predictions of C 1 and C 2 .

Original: This difference corresponds to C ’s sensitivity to the noise caused by dropout.
Revised: This difference corresponds to C ’s sensitivity to the noise caused by dropout.

Original: To measure the sensitivity d ( p 1 , p 2 ) between the two obtained probabilistic outputs, we use the symmetric kullback leibler (KL) divergence.
Revised: To measure the sensitivity d ( p 1 , p 2 ) between the two obtained probabilistic outputs, we use the symmetric kullback leibler (KL) divergence.

Original: Formally, the divergence is calculated as
Revised: Formally, the divergence is calculated as

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: Critic sampling using
Revised: Critic sampling using

Original: [Figure]
Revised: [Figure]

Original: where KL divergence between p and q is denoted as D kl ( p | q ) .
Revised: where KL divergence between p and q is denoted as D kl ( p | q ) .

Original: 3. T RAINING P ROCEDURE
Revised: 3. T RAINING P ROCEDURE

Original: In our approach, C works as both critic and classiﬁer.
Revised: In our approach, C works as both critic and classiﬁer.

Original: There are the following three requirements in our method: 1) C and G must classify source samples correctly to obtain discriminative features; 2)
Revised: There are the following three requirements in our method: 1) C and G must classify source samples correctly to obtain discriminative features; 2)

Original: C should maximize the sensitivity for target samples to detect the samples near the boundary; 3) G should learn to minimize the sensitivity to move target samples away from the boundary.
Revised: C should maximize the sensitivity for target samples to detect the samples near the boundary; 3) G should learn to minimize the sensitivity to move target samples away from the boundary.

Original: The training within the same mini-batch consists of the following three steps.
Revised: The training within the same mini-batch consists of the following three steps.

Original: Step 1 , in this step, C is trained as a classiﬁer.
Revised: Step 1 , in this step, C is trained as a classiﬁer.

Original: C and G have to classify source samples correctly to obtain discriminative features.
Revised: C and G have to classify source samples correctly to obtain discriminative features.

Original: Thus, we update both networks’ parameters based on the following standard classiﬁcation loss.
Revised: Thus, we update both networks’ parameters based on the following standard classiﬁcation loss.

Original: Given source labels y s and samples x s , the objective in this step is
Revised: Given source labels y s and samples x s , the objective in this step is

Original: [Equation]
Revised: [Equation]

Original: C ( G ( x s ))
Revised: C ( G ( x s ))

Original: k returns the probability that the sample x s is assigned to class k .
Revised: k returns the probability that the sample x s is assigned to class k .

Original: Step 2 , in this step, C is trained as a critic to detect target samples near the boundary.
Revised: Step 2 , in this step, C is trained as a critic to detect target samples near the boundary.

Original: Two classiﬁers are sampled from C for each target sample using dropout twice to obtain p 1 and p 2 .
Revised: Two classiﬁers are sampled from C for each target sample using dropout twice to obtain p 1 and p 2 .

Original: Then, C ’s parameters are updated to maximize the sensitivity as measured by Eq.
Revised: Then, C ’s parameters are updated to maximize the sensitivity as measured by Eq.

Original: Since C should learn discriminative features for source samples, in addition to the sensitivity term, we add Eq. 2.
Revised: Since C should learn discriminative features for source samples, in addition to the sensitivity term, we add Eq. 2.

Original: We experimentally conﬁrmed that this term is essential to obtain good performance.
Revised: We experimentally conﬁrmed that this term is essential to obtain good performance.

Original: [Equation]
Revised: [Equation]

Original: C 1 and C 2 are sampled from C randomly.
Revised: C 1 and C 2 are sampled from C randomly.

Original: Step 3 , in order to obtain representations where target samples are placed far from the decision boundary, G is trained to minimize sensitivity.
Revised: Step 3 , in order to obtain representations where target samples are placed far from the decision boundary, G is trained to minimize sensitivity.

Original: Here we do not add the categorical loss for source samples as in Step 2, as the generator is able to obtain discriminative features without it.
Revised: Here we do not add the categorical loss for source samples as in Step 2, as the generator is able to obtain discriminative features without it.

Original: We also assume that target samples should be uniformly distributed among the possible K classes and add a corresponding conditional entropy term to the generator objective.
Revised: We also assume that target samples should be uniformly distributed among the possible K classes and add a corresponding conditional entropy term to the generator objective.

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: [Equation]
Revised: [Equation]

Original: We update the parameters of C and G in every step following the deﬁned objectives.
Revised: We update the parameters of C and G in every step following the deﬁned objectives.

Original: We experimentally found it beneﬁcial to repeat Step 3 n times.
Revised: We experimentally found it beneﬁcial to repeat Step 3 n times.

Original: 3. I NSIGHT AND I MPROVEMENT
Revised: 3. I NSIGHT AND I MPROVEMENT

Original: Our ADR approach encourages different neurons of the classiﬁer to learn different characteristics of the input (see Sec. 4.1.)
Revised: Our ADR approach encourages different neurons of the classiﬁer to learn different characteristics of the input (see Sec. 4.1.)

Original: The output is the combination of shared and unshared nodes, therefore, to maximize the sensitivity, the unshared nodes must learn different features of target samples.
Revised: The output is the combination of shared and unshared nodes, therefore, to maximize the sensitivity, the unshared nodes must learn different features of target samples.

Original: As learning proceeds, each neuron in C will capture different characteristics.
Revised: As learning proceeds, each neuron in C will capture different characteristics.

Original: At the same time, to minimize the sensitivity, G learns to extract pure categorical information.
Revised: At the same time, to minimize the sensitivity, G learns to extract pure categorical information.

Original: If G outputs features which are not related to categorical information, such as texture, slight contrast or difference of color, C will utilize them to maximize sensitivity.
Revised: If G outputs features which are not related to categorical information, such as texture, slight contrast or difference of color, C will utilize them to maximize sensitivity.

Original: The trained classiﬁer will be sensitive to the perturbation of targets caused by dropout.
Revised: The trained classiﬁer will be sensitive to the perturbation of targets caused by dropout.

Original: We note that our approach is contrary to methods called adversarial example training (Goodfellow et al.
Revised: We note that our approach is contrary to methods called adversarial example training (Goodfellow et al.

Original: (2014b);
Revised: (2014b);

Original: Miyato et al.
Revised: Miyato et al.

Original: (2016)) which train the classiﬁer to be robust to adversarial examples.
Revised: (2016)) which train the classiﬁer to be robust to adversarial examples.

Original: They utilize input noise which can deceive or change the output of the classiﬁer, and incorporate it to obtain a good classiﬁer.
Revised: They utilize input noise which can deceive or change the output of the classiﬁer, and incorporate it to obtain a good classiﬁer.

Original: Our ADR method encourages feature generator to obtain noise-robust target features.
Revised: Our ADR method encourages feature generator to obtain noise-robust target features.

Original: However, with regard to the classiﬁer, it is trained to be sensitive to noise.
Revised: However, with regard to the classiﬁer, it is trained to be sensitive to noise.

Original: To improve the ﬁnal accuracy, we learn another classiﬁer C (cid:48) that is not trained to be sensitive to the noise.
Revised: To improve the ﬁnal accuracy, we learn another classiﬁer C (cid:48) that is not trained to be sensitive to the noise.

Original: C (cid:48) takes features generated by G and is trained with classiﬁcation loss on source samples.
Revised: C (cid:48) takes features generated by G and is trained with classiﬁcation loss on source samples.

Original: The loss of C (cid:48) is not used to update G .
Revised: The loss of C (cid:48) is not used to update G .

Original: We compare the accuracy of C and C (cid:48) in experiments on image classiﬁcation.
Revised: We compare the accuracy of C and C (cid:48) in experiments on image classiﬁcation.

Original:  E XPERIMENTS 4. E XPERIMENT ON T OY DATA
Revised:  E XPERIMENTS 4. E XPERIMENT ON T OY DATA

Original: Experimental Setting.
Revised: Experimental Setting.

Original: In this experiment, we observe the decision boundary obtained by each neuron to demonstrate that ADR encourages the neurons to learn different input characteristics.
Revised: In this experiment, we observe the decision boundary obtained by each neuron to demonstrate that ADR encourages the neurons to learn different input characteristics.

Original: We use synthetic “two moons” data for this problem.
Revised: We use synthetic “two moons” data for this problem.

Original: Two dimensional samples from two classes are generated as source samples.
Revised: Two dimensional samples from two classes are generated as source samples.

Original: Target samples are obtained by rotating the source samples.
Revised: Target samples are obtained by rotating the source samples.

Original: In our setting, the rotation was set to 30 degrees and data was generated with scikit-learn (Pedregosa et al.
Revised: In our setting, the rotation was set to 30 degrees and data was generated with scikit-learn (Pedregosa et al.

Original: We train a six-layered fully-connected network; the lower 3 layers are used as feature generator, and upper 3 layers are used as classiﬁer.
Revised: We train a six-layered fully-connected network; the lower 3 layers are used as feature generator, and upper 3 layers are used as classiﬁer.

Original: We used Batch Normalization (Ioffe & Szegedy (2015)) and ReLU as activation function.
Revised: We used Batch Normalization (Ioffe & Szegedy (2015)) and ReLU as activation function.

Original: The number of neurons are [2,5,5] for feature generator, [5,5,2] for classiﬁer.
Revised: The number of neurons are [2,5,5] for feature generator, [5,5,2] for classiﬁer.

Original: We visualize the boundary obtained from each neuron in last layer by removing the output of all other neurons.
Revised: We visualize the boundary obtained from each neuron in last layer by removing the output of all other neurons.

Original: [Figure]
Revised: [Figure]

Original: Results.
Revised: Results.

Original: We show the learned boundary in Fig.
Revised: We show the learned boundary in Fig.

Original: In the baseline model trained only with source samples (top row), two of ﬁve neurons do not seem to learn an effective boundary, and three neurons learn a similar boundary.
Revised: In the baseline model trained only with source samples (top row), two of ﬁve neurons do not seem to learn an effective boundary, and three neurons learn a similar boundary.

Original: On the other hand, in our method (bottom row), although two neurons do not seem to learn any meaningful boundary, three neurons learn a distinctive boundaries.
Revised: On the other hand, in our method (bottom row), although two neurons do not seem to learn any meaningful boundary, three neurons learn a distinctive boundaries.

Original: Each neuron is trained to be sensitive to the noise caused by target samples.
Revised: Each neuron is trained to be sensitive to the noise caused by target samples.

Original: The ﬁnal decision boundary (rightmost column) classiﬁes most target samples correctly.
Revised: The ﬁnal decision boundary (rightmost column) classiﬁes most target samples correctly.

Original: The accuracy of our proposed method is 96% whereas the accuracy of the non-adapted model was 84%.
Revised: The accuracy of our proposed method is 96% whereas the accuracy of the non-adapted model was 84%.

Original: 4. U NSUPERVISED D OMAIN A DAPTATION FOR C LASSIFICATION
Revised: 4. U NSUPERVISED D OMAIN A DAPTATION FOR C LASSIFICATION

Original: Experiments on Digits Classiﬁcation.
Revised: Experiments on Digits Classiﬁcation.

Original: We evaluate our model on adaptation between digits datasets.
Revised: We evaluate our model on adaptation between digits datasets.

Original: We use MNIST (LeCun et al.
Revised: We use MNIST (LeCun et al.

Original: , SVHN (Netzer et al.
Revised: , SVHN (Netzer et al.

Original: and USPS datasets and follow the protocol of unsupervised domain adaptation used by (Tzeng et al.
Revised: and USPS datasets and follow the protocol of unsupervised domain adaptation used by (Tzeng et al.

Original: To extensively compare our method with previous methods, in adaptation from MNIST to USPS, we applied our method to different protocol used in Bousmalis et al.
Revised: To extensively compare our method with previous methods, in adaptation from MNIST to USPS, we applied our method to different protocol used in Bousmalis et al.

Original: We assume no labeled target samples and use ﬁxed hyper-parameters for all experiments, unlike other works that use a target validation set (Saito et al.
Revised: We assume no labeled target samples and use ﬁxed hyper-parameters for all experiments, unlike other works that use a target validation set (Saito et al.

Original: The number of iterations for Step 3 was ﬁxed at n = 4 .
Revised: The number of iterations for Step 3 was ﬁxed at n = 4 .

Original: We used the same network architecture as in (Tzeng et al.
Revised: We used the same network architecture as in (Tzeng et al.

Original: but inserted a Batch Normalization layer before the activation layer to stabilize the training.
Revised: but inserted a Batch Normalization layer before the activation layer to stabilize the training.

Original: We used Adam (Kingma & Ba (2014)) for optimizer and set the learning rate to 2 . 

Revised: We used Adam (Kingma & Ba (2014)) for optimizer and set the learning rate to 2 .

Original: × 10 − 4 .
Revised: × 10 − 4 .

Original: We compare our approach to several existing methods and to the entropy minimization baseline (ENT) obtained by modifying (Springenberg (2015)).
Revised: We compare our approach to several existing methods and to the entropy minimization baseline (ENT) obtained by modifying (Springenberg (2015)).

Original: As we mentioned in Section 2, we trained a model that generates features to minimize the entropy of the output probability for target samples.
Revised: As we mentioned in Section 2, we trained a model that generates features to minimize the entropy of the output probability for target samples.

Original: The detail of the baseline is written in our supplemental material.
Revised: The detail of the baseline is written in our supplemental material.

Original: Due to space limitations, we provide a detailed explanation of this baseline in the appendix.
Revised: Due to space limitations, we provide a detailed explanation of this baseline in the appendix.

Original: Results in Table 1 demonstrate that ADR obtains better performance than existing methods.
Revised: Results in Table 1 demonstrate that ADR obtains better performance than existing methods.

Original: In particular, on the challenging adaptation task from SVHN to MNIST, our method achieves much better accuracy than previously reported.
Revised: In particular, on the challenging adaptation task from SVHN to MNIST, our method achieves much better accuracy than previously reported.

Original: Fig.
Revised: Fig.

Original: 5 shows the learning curve of each experiment.
Revised: 5 shows the learning curve of each experiment.

Original: As sensitivity loss increases, the target accuracy improves.
Revised: As sensitivity loss increases, the target accuracy improves.

Original: This means that as critic C learns to detect the non-discriminative samples, feature generator G learns to fool it, resulting in improved accuracy.
Revised: This means that as critic C learns to detect the non-discriminative samples, feature generator G learns to fool it, resulting in improved accuracy.

Original: In addition, we can see that the sensitivity of source samples increases too.
Revised: In addition, we can see that the sensitivity of source samples increases too.

Original: As mentioned in the Sec 3.3, the critic network should learn to capture features which are not very important for classiﬁcation, such as texture or slight edges, and it seems to also capture such information in source samples.
Revised: As mentioned in the Sec 3.3, the critic network should learn to capture features which are not very important for classiﬁcation, such as texture or slight edges, and it seems to also capture such information in source samples.

Original: The accuracy of the classiﬁer C (cid:48) (denoted by red), which is trained not to be sensitive to the noise, is almost always better than the accuracy of the critic network.
Revised: The accuracy of the classiﬁer C (cid:48) (denoted by red), which is trained not to be sensitive to the noise, is almost always better than the accuracy of the critic network.

Original: In adaptation from
Revised: In adaptation from

Original: SVHN to MNIST (Fig. 5(c)), the accuracy of the critic often suffers as it becomes too sensitive to the noise caused by dropout.
Revised: SVHN to MNIST (Fig. 5(c)), the accuracy of the critic often suffers as it becomes too sensitive to the noise caused by dropout.

Original: On the other hand, the accuracy shown by the red line is stable.
Revised: On the other hand, the accuracy shown by the red line is stable.

Original: Our
Revised: Our

Original: ENT baseline shows good performance compared to other existing methods.
Revised: ENT baseline shows good performance compared to other existing methods.

Original: This result indicates the effectiveness of methods based on entropy minimization.
Revised: This result indicates the effectiveness of methods based on entropy minimization.

Original: In Fig.
Revised: In Fig.

Original: 4, we compare our proposed method and ENT in terms of entropy of target samples.
Revised: 4, we compare our proposed method and ENT in terms of entropy of target samples.

Original: Our method clearly decreases the entropy, because target samples are moved away from the decision boundary.
Revised: Our method clearly decreases the entropy, because target samples are moved away from the decision boundary.

Original: Yet, its behavior is different from ENT.
Revised: Yet, its behavior is different from ENT.

Original: Interestingly, the entropy is made smaller than ENT in case of adaptation from USPS to
Revised: Interestingly, the entropy is made smaller than ENT in case of adaptation from USPS to

Original: MNIST (Fig. 4(b)) though ENT directly minimizes the entropy and our method does not.
Revised: MNIST (Fig. 4(b)) though ENT directly minimizes the entropy and our method does not.

Original: On the
Revised: On the

Original: SVHN to MNIST task (Fig. 4(c)), the entropy of ADR is larger than ENT, which indicates that our method places the target samples a closer to boundary than does.
Revised: SVHN to MNIST task (Fig. 4(c)), the entropy of ADR is larger than ENT, which indicates that our method places the target samples a closer to boundary than does.

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Table]
Revised: [Table]

Original: MNIST(P1) and MNIST(P2) indicate different experimental settings used in Tzeng et al.
Revised: MNIST(P1) and MNIST(P2) indicate different experimental settings used in Tzeng et al.

Original: (2017) and
Revised: (2017) and

Original: Bousmalis et al.
Revised: Bousmalis et al.

Original: (2017) respectively.
Revised: (2017) respectively.

Original: Experiments on Object Classiﬁcation.
Revised: Experiments on Object Classiﬁcation.

Original: We evaluate our method on ﬁne-tuning a pretrained CNN.
Revised: We evaluate our method on ﬁne-tuning a pretrained CNN.

Original: We use a new domain adaptation benchmark called the VisDA Challenge (Peng et al.
Revised: We use a new domain adaptation benchmark called the VisDA Challenge (Peng et al.

Original: (2017)) which focuses on the challenging task of adapting from synthetic to real images.
Revised: (2017)) which focuses on the challenging task of adapting from synthetic to real images.

Original: The source domain consists of 152,409 synthetic 2D images from 12 object classes rendered from 3D models.
Revised: The source domain consists of 152,409 synthetic 2D images from 12 object classes rendered from 3D models.

Original: The validation and test target domains consists of real images, which belong to the same classes.
Revised: The validation and test target domains consists of real images, which belong to the same classes.

Original: We used the validation domain (55,400 images) as our target domain in an unsupervised domain adaptation setting.
Revised: We used the validation domain (55,400 images) as our target domain in an unsupervised domain adaptation setting.

Original: We evaluate our model on ﬁne-tuning networks pretrained on ImageNet (Deng et al.
Revised: We evaluate our model on ﬁne-tuning networks pretrained on ImageNet (Deng et al.

Original: ResNet101 (He et al.
Revised: ResNet101 (He et al.

Original: and ResNext (Xie et al. (2016)).
Revised: and ResNext (Xie et al. (2016)).

Original: For the feature generator, we use the pre-trained CNN after removing the top fully connected layer.
Revised: For the feature generator, we use the pre-trained CNN after removing the top fully connected layer.

Original: For the classiﬁcation network, we use a three-layered fully connected network.
Revised: For the classiﬁcation network, we use a three-layered fully connected network.

Original: Table 2 shows that our method outperformed other distribution matching methods and our new baseline (ENT) in ﬁnetuning both networks by a large margin.
Revised: Table 2 shows that our method outperformed other distribution matching methods and our new baseline (ENT) in ﬁnetuning both networks by a large margin.

Original: ENT did not achieve better performance than existing methods, though improvement over the source only model was observed.
Revised: ENT did not achieve better performance than existing methods, though improvement over the source only model was observed.

Original: Although this method performed well on digits, it does not work as well here, possibly because of the larger shift between very different domains.
Revised: Although this method performed well on digits, it does not work as well here, possibly because of the larger shift between very different domains.

Original: In this experiment, after training G and C , we retrained a classiﬁer C (cid:48) just on the features generated by G due to GPU memory limitations, and observed improvement in both networks.
Revised: In this experiment, after training G and C , we retrained a classiﬁer C (cid:48) just on the features generated by G due to GPU memory limitations, and observed improvement in both networks.

Original: Fig.
Revised: Fig.

Original: 6 visualizes the target features obtained by G with the pretrained model, model ﬁne-tuned on source, and our ADR method.
Revised: 6 visualizes the target features obtained by G with the pretrained model, model ﬁne-tuned on source, and our ADR method.

Original: While the embedding of the source only model does not separate classes well due to domain shift, we can see clearly improved separation with ADR.
Revised: While the embedding of the source only model does not separate classes well due to domain shift, we can see clearly improved separation with ADR.

Original: Image Segmentation experiments.
Revised: Image Segmentation experiments.

Original: We apply our method to adaptation for semantic image segmentation.
Revised: We apply our method to adaptation for semantic image segmentation.

Original: Image segmentation is different from classiﬁcation in that we classify each pixel in the
Revised: Image segmentation is different from classiﬁcation in that we classify each pixel in the

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: image.
Revised: image.

Original: To evaluate the performance on segmentation, the synthetic GTA5 (Richter et al.
Revised: To evaluate the performance on segmentation, the synthetic GTA5 (Richter et al.

Original: dataset is used as source, and real CityScape (Cordts et al.
Revised: dataset is used as source, and real CityScape (Cordts et al.

Original: dataset is used as target.
Revised: dataset is used as target.

Original: Previous work tackled this problem by matching distributions of each pixel’s feature in a middle layer of the network (Hoffman et al.
Revised: Previous work tackled this problem by matching distributions of each pixel’s feature in a middle layer of the network (Hoffman et al.

Original: In this work, we apply ADR by calculating sensitivity between all pixels.
Revised: In this work, we apply ADR by calculating sensitivity between all pixels.

Original: The training procedure is exactly the same as in classiﬁcation experiments.
Revised: The training procedure is exactly the same as in classiﬁcation experiments.

Original: We use the pretrained ResNet50, and utilize an FCN (Long et al.
Revised: We use the pretrained ResNet50, and utilize an FCN (Long et al.

Original: (2015a))
Revised: (2015a))

Original: based network architecture.
Revised: based network architecture.

Original: For the feature generator, we use the pretrained network without fully-connected layers.
Revised: For the feature generator, we use the pretrained network without fully-connected layers.

Original: For the classiﬁer, we use a fully-convolutional network with dropout layers.
Revised: For the classiﬁer, we use a fully-convolutional network with dropout layers.

Original: Due to limited memory, the batch size is set to 1.
Revised: Due to limited memory, the batch size is set to 1.

Original: We include details of the network architecture in our supplementary material.
Revised: We include details of the network architecture in our supplementary material.

Original: For comparison, we train a domain classiﬁer based model for our network (DANN).
Revised: For comparison, we train a domain classiﬁer based model for our network (DANN).

Original: We build a domain classiﬁer network for the features of each pixel following (Hoffman et al.
Revised: We build a domain classiﬁer network for the features of each pixel following (Hoffman et al.

Original: In Table 3, we show the qualitative comparison with existing methods.
Revised: In Table 3, we show the qualitative comparison with existing methods.

Original: ADR clearly improves mean
Revised: ADR clearly improves mean

Original: IoU compared to the source-only and competing models.
Revised: IoU compared to the source-only and competing models.

Original: We illustrate the improvement on example input images, ground truth labels, images segmented by Source Only model and our method in
Revised: We illustrate the improvement on example input images, ground truth labels, images segmented by Source Only model and our method in

Original: Fig.
Revised: Fig.

Original: While the Source Only model seems to suffer from domain shift, ADR generates a clean segmentation.
Revised: While the Source Only model seems to suffer from domain shift, ADR generates a clean segmentation.

Original: These experiments demonstrate the effectiveness of ADR on semantic segmentation.
Revised: These experiments demonstrate the effectiveness of ADR on semantic segmentation.

Original: Although we implemented ENT in this setting, the accuracy was much worse than the Source Only model with a mIoU of 15.0.
Revised: Although we implemented ENT in this setting, the accuracy was much worse than the Source Only model with a mIoU of 15.0.

Original: The ENT method does not seem to work well on synthetic-to-real shifts.
Revised: The ENT method does not seem to work well on synthetic-to-real shifts.

Original: [Table] [Table]
Revised: [Table] [Table]

Original: 4. S EMI - SUPERVISED LEARNING USING GAN S
Revised: 4. S EMI - SUPERVISED LEARNING USING GAN S

Original: In this section, we demonstrate how to apply our method in training a Generative Adversarial Network (GAN) applied to semi-supervised learning.
Revised: In this section, we demonstrate how to apply our method in training a Generative Adversarial Network (GAN) applied to semi-supervised learning.

Original: We follow the method proposed by (Springenberg (2015); Salimans et al.
Revised: We follow the method proposed by (Springenberg (2015); Salimans et al.

Original: (2016)), who use a K -class classiﬁcation network as a critic to train a GAN in the semi-supervised setting.
Revised: (2016)), who use a K -class classiﬁcation network as a critic to train a GAN in the semi-supervised setting.

Original: Approach.
Revised: Approach.

Original: In contrast to the domain adaptation setting, here G tries to generate images which fool the critic C .
Revised: In contrast to the domain adaptation setting, here G tries to generate images which fool the critic C .

Original: Also, in this setting, we are given labeled and unlabeled real images from the same domain.
Revised: Also, in this setting, we are given labeled and unlabeled real images from the same domain.

Original: Then, we train the critic to classify labeled images correctly and to move unlabeled images far from the decision boundary.
Revised: Then, we train the critic to classify labeled images correctly and to move unlabeled images far from the decision boundary.

Original: To achieve this, we propose to train the critic with the following objective:
Revised: To achieve this, we propose to train the critic with the following objective:

Original: [Equation]
Revised: [Equation]

Original: where X L denotes the subset of labeled samples, X u denotes unlabeled ones and X g denotes images generated by G and H denotes entropy as Eq.6 shows.
Revised: where X L denotes the subset of labeled samples, X u denotes unlabeled ones and X g denotes images generated by G and H denotes entropy as Eq.6 shows.

Original: The critic is trained to minimize the loss on labeled samples in the ﬁrst term.
Revised: The critic is trained to minimize the loss on labeled samples in the ﬁrst term.

Original: Since unlabeled images should be far away from the decision boundary and should be distributed uniformly among the classes, we add the second and fourth term.
Revised: Since unlabeled images should be far away from the decision boundary and should be distributed uniformly among the classes, we add the second and fourth term.

Original: The third term encourages the critic to detect fake images generated near the boundary.
Revised: The third term encourages the critic to detect fake images generated near the boundary.

Original: The objective of G is as follows,
Revised: The objective of G is as follows,

Original: [Equation]
Revised: [Equation]

Original: where the second term encourages generated images to be similar to real images, which is known to be effective to stabilize the training.
Revised: where the second term encourages generated images to be similar to real images, which is known to be effective to stabilize the training.

Original: The ﬁrst term encourages the generator to create fake images which should be placed far away from the boundary.
Revised: The ﬁrst term encourages the generator to create fake images which should be placed far away from the boundary.

Original: Such images should be similar to real images because they are likely to be assigned to some class with high probability.
Revised: Such images should be similar to real images because they are likely to be assigned to some class with high probability.

Original: Here, we update C and
Revised: Here, we update C and

Original: G same number of times.
Revised: G same number of times.

Original: Experiment.
Revised: Experiment.

Original: We evaluate our proposed GAN training method by using SVHN and CIFAR datasets, using the critic network architecture from (Salimans et al.
Revised: We evaluate our proposed GAN training method by using SVHN and CIFAR datasets, using the critic network architecture from (Salimans et al.

Original: In the experiment on
Revised: In the experiment on

Original: SVHN, we replaced Weight Normalization with Batch Normalization for C .
Revised: SVHN, we replaced Weight Normalization with Batch Normalization for C .

Original: Also, in the experiment
Revised: Also, in the experiment

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: Labeled Only
Revised: Labeled Only

Original: [Table]
Revised: [Table]

Revised: Maaløe et al

Original:  on CIFAR10, we construct a classiﬁer from a middle layer of the critic, which is not incorporated into the adversarial training step.
Revised: [Table] [Table] on CIFAR10, we construct a classiﬁer from a middle layer of the critic, which is not incorporated into the adversarial training step.

Original: This is motivated by the insight that the critic in our method is trained to be too sensitive to the dropout noise as we explained in Sec 3.3.
Revised: This is motivated by the insight that the critic in our method is trained to be too sensitive to the dropout noise as we explained in Sec 3.3.

Original: Results.
Revised: Results.

Original: From Fig. 8(a), we can see that ADR seems to generate realistic SVHN images.
Revised: From Fig. 8(a), we can see that ADR seems to generate realistic SVHN images.

Original: Some images are signiﬁcantly blurred, but most of  images are clear and diverse.
Revised: Some images are signiﬁcantly blurred, but most of the images are clear and diverse.

Original: As for generated CIFAR images, they do not seem as realistic, but some objects appear in most images.
Revised: As for generated CIFAR10 images, they do not seem as realistic, but some objects appear in most images.

Original: In Table 4, we can see that the accuracy of the critic trained by our method has better performance than other models for SVHN.
Revised: In Table 4, we can see that the accuracy of the critic trained by our method has better performance than other models for SVHN.

Original: For CIFAR10, the accuracy was a little worse than other state-of-the-art methods.
Revised: For CIFAR10, the accuracy was slightly worse than other state-of-the-art methods.

Original: We could show how to apply our method to SSL and the results are mixed.
Revised: We conclude that, despite its clear advantage on the domain adaptation tasks, our method produces mixed results on the SSL tasks.

Original: The method could still be useful for SSL. However, it needs further exploration to improve the accuracy.
Revised: It could still be useful for SSL, however, it needs further exploration to improve the accuracy.

Original: In Eq. 6, we propose to maximize the entropy of  marginal class distribution of  unlabeled real images as well as forcing them to be far from the boundary.
Revised: For example, in Eq. 6, we propose to maximize the entropy of the marginal class distribution of the unlabeled real images, as well as forcing them to be far from the boundary.

Original: However, these objectives seem to contradict each other, which may  degrade the performance.
Revised: However, these objectives may contradict each other, which may in turn degrade the performance.

Original:  Dai et al.
Revised: In late-breaking results, Dai et al.

Original: (2017) theoretically showed that just generating fake images that are far from decision boundaries does not help improving accuracy in training GAN in the setting of SSL.
Revised: (2017) theoretically showed that just generating fake images that are far from decision boundaries does not help to improve accuracy in training GANs in the setting of SSL.

Original: On the other hand, our proposed training objective forces fake images to be far from boundaries, which could be improved seeing the result of Dai et al.
Revised: Further improvement of our SSL approach based on these results is an interesting direction for future work.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: In this paper, we introduced a novel approach for aligning feature distributions called Adversarial
Revised: In this paper, we introduced a novel approach for aligning feature distributions called Adversarial

Original: Dropout Regularization, which learns to generate discriminative features for the target domain.
Revised: Dropout Regularization, which learns to generate discriminative features for the target domain.

Original: The method consists of a critic network that can detect samples near the boundary and a feature generator that fools the critic.
Revised: The method consists of a critic network that can detect samples near the boundary and a feature generator that fools the critic.

Original: Our approach is general, applies to a variety of tasks, and does not require domain labels.
Revised: Our approach is general, applies to a variety of tasks, and does not require domain labels.

Original: In extensive domain adaptation experiments, our method outperformed baseline methods, including entropy minimization, and achieved state-of-the-art results on three datasets.
Revised: In extensive domain adaptation experiments, our method outperformed baseline methods, including entropy minimization, and achieved state-of-the-art results on three datasets.

Original: We also showed how to apply our method to train Generative Adversarial Networks for semi-supervised learning.
Revised: We also showed how to apply our method to train Generative Adversarial Networks for semi-supervised learning.

Original: A PPENDIX A E NTROPY B ASED M ETHOD FOR D OMAIN A DAPTATION
Revised: A PPENDIX A E NTROPY B ASED M ETHOD FOR D OMAIN A DAPTATION

Original: Our method aims to move target samples away from the decision boundary.
Revised: Our method aims to move target samples away from the decision boundary.

Original: Some techniques used in training Generative Adversarial Networks can be applied to achieve our goal too.
Revised: Some techniques used in training Generative Adversarial Networks can be applied to achieve our goal too.

Original: (Springenberg (2015); Salimans et al.
Revised: (Springenberg (2015); Salimans et al.

Original: (2016)) used small number of labeled samples to train critic.
Revised: (2016)) used small number of labeled samples to train critic.

Original: Critic is trained to classify real samples into K classes.
Revised: Critic is trained to classify real samples into K classes.

Original: They also trained critic to move unlabeled real images away from the boundary by minimizing entropy of the critic’s output.
Revised: They also trained critic to move unlabeled real images away from the boundary by minimizing entropy of the critic’s output.

Original: Generated fake images are moved near the boundary by maximizing the entropy.
Revised: Generated fake images are moved near the boundary by maximizing the entropy.

Original: On the other hand, generator is trained to generate fake images which should be placed away from the boundary.
Revised: On the other hand, generator is trained to generate fake images which should be placed away from the boundary.

Original: This kind of method can be easily applied to domain adaptation problem.
Revised: This kind of method can be easily applied to domain adaptation problem.

Original: We would like to describe the method along with our problem setting.
Revised: We would like to describe the method along with our problem setting.

Original: Similar to our method, we have critic networks C and generator G .
Revised: Similar to our method, we have critic networks C and generator G .

Original: C classiﬁes samples into K class.
Revised: C classiﬁes samples into K class.

Original: C is trained to maximize the entropy of target samples, which encourages to move the target samples near the boundary.
Revised: C is trained to maximize the entropy of target samples, which encourages to move the target samples near the boundary.

Original: Then, G is trained to minimize the entropy of them.
Revised: Then, G is trained to minimize the entropy of them.

Original: Thus, G tries to move target samples away from the boundary.
Revised: Thus, G tries to move target samples away from the boundary.

Original: The only difference from our method is that we used entropy term for adversarial training loss.
Revised: The only difference from our method is that we used entropy term for adversarial training loss.

Original: That is, in this method, we replace our sensitivity term
Revised: That is, in this method, we replace our sensitivity term

Original: d ( p 1 , p 2 ) in Eq.
Revised: d ( p 1 , p 2 ) in Eq.

Original: 4 with entropy of the classiﬁer output.
Revised: 4 with entropy of the classiﬁer output.

Original: The adversarial loss for this baseline method is a following one.
Revised: The adversarial loss for this baseline method is a following one.

Original: [Equation]
Revised: [Equation]

Original: [Equation]

Original: The hyperparameter n , how many times we update G for adversarial loss in one mini-batch, is set as n = 4 .
Revised: The hyperparameter n , how many times we update G for adversarial loss in one mini-batch, is set as n = 4 .

Original: Experimentally, it worked well for all settings.
Revised: Experimentally, it worked well for all settings.

Original: B D IGITS C LASSIFICATION T RAINING D ETAIL
Revised: B D IGITS C LASSIFICATION T RAINING D ETAIL

Original: We follow the protocol used in (Tzeng et al.
Revised: We follow the protocol used in (Tzeng et al.

Original: For adaptation from SVHN to MNIST, we used standard training splits of each datasets as training data.
Revised: For adaptation from SVHN to MNIST, we used standard training splits of each datasets as training data.

Original: For evaluation, we used test splits of MNIST.
Revised: For evaluation, we used test splits of MNIST.

Original: For the adaptation between MNIST and USPS (P1), we sampled 2000 images from
Revised: For the adaptation between MNIST and USPS (P1), we sampled 2000 images from

Original: MNIST and 1800 images from USPS.
Revised: MNIST and 1800 images from USPS.

Original: For the adaptation between MNIST and USPS (P2), we used all training images of MNIST and USPS following Bousmalis et al.
Revised: For the adaptation between MNIST and USPS (P2), we used all training images of MNIST and USPS following Bousmalis et al.

Original: In these experiments, we composed the mini-batch half from source and half from target samples.
Revised: In these experiments, we composed the mini-batch half from source and half from target samples.

Original: The batchsize was set as 128 for both source and target.
Revised: The batchsize was set as 128 for both source and target.

Original: We report the score after repeating Step 1 ∼ 3 (please see Sec 3.2) 20000 times.
Revised: We report the score after repeating Step 1 ∼ 3 (please see Sec 3.2) 20000 times.

Original: For our baseline, ENT, we used the same network architecture and the same hyper-parameters as used in our proposed method.
Revised: For our baseline, ENT, we used the same network architecture and the same hyper-parameters as used in our proposed method.

Original: C O BJECT C LASSIFICATION T RAINING D ETAIL
Revised: C O BJECT C LASSIFICATION T RAINING D ETAIL

Original: In this experiment, SGD with learning rate 1 . 

Revised: In this experiment, SGD with learning rate 1 .

Original: × 10 − 3 is used to optimize the parameters.
Revised: × 10 − 3 is used to optimize the parameters.

Original: For the ﬁnetuning of ResNet101, we set batch-size as 32.
Revised: For the ﬁnetuning of ResNet101, we set batch-size as 32.

Original: Due to the limit of GPU memory, we set it asin ﬁnetuning ResNext model.
Revised: Due to the limit of GPU memory, we set it asin ﬁnetuning ResNext model.

Original: We report the score after 20 epochs training.
Revised: We report the score after 20 epochs training.

Original: In order to train MMD model, we use 5 RBF kernels with the following standard deviation parameters:
Revised: In order to train MMD model, we use 5 RBF kernels with the following standard deviation parameters:

Original: [Equation]
Revised: [Equation]

Original: We changed the number of the kernels and their parameters, but we could not observe signiﬁcant performance difference.
Revised: We changed the number of the kernels and their parameters, but we could not observe signiﬁcant performance difference.

Original: We report the performance after 5 epochs.
Revised: We report the performance after 5 epochs.

Original: We could not see any improvement after the epoch.
Revised: We could not see any improvement after the epoch.

Original: To train a model (Ganin & Lempitsky (2014)), we used two-layered domain classiﬁcation networks.
Revised: To train a model (Ganin & Lempitsky (2014)), we used two-layered domain classiﬁcation networks.

Original: Experimentally, we did not see any improvement when the network architecture is changed.
Revised: Experimentally, we did not see any improvement when the network architecture is changed.

Original: According to the original method (Ganin & Lempitsky (2014)), learning rate is decreased every iteration.
Revised: According to the original method (Ganin & Lempitsky (2014)), learning rate is decreased every iteration.

Original: However, in our experiment, we could not see improvement, thus, we ﬁxed learning rate 1 . 

Revised: However, in our experiment, we could not see improvement, thus, we ﬁxed learning rate 1 .

Original: × 10 − 3 .
Revised: × 10 − 3 .

Original: We report the accuracy after 1 epoch.
Revised: We report the accuracy after 1 epoch.

Original: The accuracy dropped signiﬁcantly after the ﬁrst epoch.
Revised: The accuracy dropped signiﬁcantly after the ﬁrst epoch.

Original: We assume this is due to the large domain difference between synthetic and real images.
Revised: We assume this is due to the large domain difference between synthetic and real images.

Original: For our new baseline, ENT, we used the same hyperparameter as we used for our proposed method.
Revised: For our new baseline, ENT, we used the same hyperparameter as we used for our proposed method.

Original: Since the accuracy of ENT drops signiﬁcantly after around 5 epochs, we report the accuracy afterepoch updates.
Revised: Since the accuracy of ENT drops signiﬁcantly after around 5 epochs, we report the accuracy afterepoch updates.

Original: D S EGMENTATION E XPERIMENTS D ETAIL
Revised: D S EGMENTATION E XPERIMENTS D ETAIL

Original: [Figure]
Revised: [Figure]

Original: We modiﬁed FCN Long et al.
Revised: We modiﬁed FCN Long et al.

Original: (2015a) architecture suitable for ResNet structure.
Revised: (2015a) architecture suitable for ResNet structure.

Original: The features from ResBlock 2 ∼ 4 and the ﬁrst convolution layer and maxpooling layer are used in our implementation.
Revised: The features from ResBlock 2 ∼ 4 and the ﬁrst convolution layer and maxpooling layer are used in our implementation.

Original: In Fig.
Revised: In Fig.

Original: 9, we show how we integrated the features of each layers.
Revised: 9, we show how we integrated the features of each layers.

Original: We regard the layers of ResNet50 as generator and rest of the networks, namely convolution and upsampling layers as a critic network.
Revised: We regard the layers of ResNet50 as generator and rest of the networks, namely convolution and upsampling layers as a critic network.

Original: The input images were resized to 512x1024 due to the limit of GPU memory.
Revised: The input images were resized to 512x1024 due to the limit of GPU memory.

Original: For the same reason, the batchsize was set to one.
Revised: For the same reason, the batchsize was set to one.

Original: E GAN TRAINING
Revised: E GAN TRAINING

Original: We used the critic architecture proposed by Salimans et al.
Revised: We used the critic architecture proposed by Salimans et al.

Original: We set the batch size asand used Adam with learning rate 2 . 

Revised: We set the batch size asand used Adam with learning rate 2 .

Original: × 1 . 0 − 4 for optimizer.
Revised: × 1 . 0 − 4 for optimizer.

Original: After the conv6 layer of the critic, we constructed a classiﬁer which was not concerned with adversarial learning process.
Revised: After the conv6 layer of the critic, we constructed a classiﬁer which was not concerned with adversarial learning process.