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Title: Censoring Representations with Multiple-Adversaries over Random Subspaces

Paper content:
Adversarial feature learning (AFL) is one of the promising ways for explicitly constrains neural networks to learn desired representations; for example, AFL could help to learn anonymized representations so as to avoid privacy issues.
AFL learn such a representations by training the networks to deceive the adversary that predict the sensitive information from the network, and therefore, the success of the AFL heavily relies on the choice of the adversary.
This paper proposes a novel design of the adversary, {\em multiple adversaries over random subspaces} (MARS) that instantiate the concept of the {\em volunerableness}.
The proposed method is motivated by an assumption that deceiving an adversary could fail to give meaningful information if the adversary is easily fooled, and adversary rely on single classifier suffer from this issues.
In contrast, the proposed method is designed to be less vulnerable, by utilizing the ensemble of independent classifiers where each classifier tries to predict sensitive variables from a different {\em subset} of the representations.
The empirical validations on three user-anonymization tasks show that our proposed method achieves state-of-the-art performances in all three datasets without significantly harming the utility of data.
This is significant because it gives new implications about designing the adversary, which is important to improve the performance of AFL.
Since its invention over ten years ago BID4 , deep neural networks (DNN) have shown significant performance improvements in various fields.
When we apply DNN or more general machine learning techniques to real-world data, one of the key challenges is how to systematically incorporate the desired constraints into the learned representations in a controllable manner.
For example, when practitioners apply these techniques to the data that contain a lot of user information (such as images with username BID1 or data of wearables BID6 ), the desired representations should not contain user-information that may result in privacy issues.
Moreover, for legal and ethical reasons, machine learning algorithms have to make fair decisions, which do not rely on sensitive variables such as gender, age, or race BID8 BID1 .
Such a background requires removal of information related to specific factors (such as user ID, race, etc.) from the representation; this is called censoring representations in this paper.One of the recently proposed approaches for censoring representation is adversarial feature learning (AFL) BID1 BID6 BID13 , which employs the adversarial training framework to constrain the representations FIG0 .
Specifically, AFL considers an adversarial classifier who attempts to predict sensitive variables from the representations of a DNN and simultaneously trains the DNN to deceive the classifier.
By alternatively or jointly (using gradient reversal layer proposed by BID2 ) training the adversary and DNN in such a manner, AFL ensures that there is little or no information about the sensitive variables in the representations.Although some previous studies report significant performance improvements of the AFL in the context of censoring representations, the success of the AFL depends on the choice of the adversarial classifier.
For example, if we use a logistic regression as the adversarial classifier, AFL can only eliminate the information that is linearly separated in the representation spaces and cannot remove any non-linear dependency.
It is also possible that deceiving some classifier might be too easy, resulting in poor performance improvements of AFL.
As such, the design of adversary is crucial for the performance of AFL; however, existing studies fail to address how to design the adversary for improving the quality of AFL.In this paper, we propose a novel design of adversary for improving the performance of AFL, multiple-adversaries over random subspace (MARS), which consider the vulnerableness of the adversary.
The proposed design is motivated by the recent report BID6 that is just increasing the capacity of adversary did not successfully improves the performance of AFL BID6 , and assumptions that deceiving an adversary fail to give meaningful information if the adversary is easily fooled, and adversary relies on single classifier suffer from this issues.
The proposed method incorporates multiple adversaries where each adversary tries to predict sensitive variables from a different subset of the representations.
This design makes adversary less vulnerable to the update of the encoder since the encoder needs to in a set of diverse adversaries.
In this paper, we validate the effectiveness of the proposed design by empirically showing that (1) MARS archives better performance compared to baselines (that uses a single adversary and multiple adversaries over the entire representation spaces), and (2) MARS is less vulnerable compared to the baselines.The primary contributions of this paper are as follows:• This is the first study verifying the importance of the design of adversary in AFL and proposes the novel design for improving AFL.
This is significant because the results suggest that design of adversary is vital for the performance of adversary, and gives new implications about designing the adversary in AFL, which is important to improve the performance of AFL.
It is worth mentioning that, except our paper, all existing studies focus only on the accuracy/capacity for designing adversaries, which is not enough for improving the performance of AFL as shown in this paper.•
The proposed method achieved state-of-the-art performance in the task of censoring representations, which is essential to extend the applicability of DNN to many real-world applications. The
empirical validation using three user-anonymization tasks shows that the proposed method allows the learning of significantly more anonymized representations with negligible performance degradation. Specifically
, the probability of correctly predicting the user ID from learned representations is more than 0.07 points better on average than that of a single adversary and multiple adversaries over entire representation spaces.2 PROBLEM DEFINITION
AND RELATED WORKS 2.1 PROBLEM DEFINITION: CENSORING REPRESENTATIONS Censoring representation is a task to obtaining unbiased features. Here, unbiased features
are features that are less affected by S, where S is a random variable that we want to remove from the data for some reason. One typical reason is related
to fairness or privacy, which requires the output of neural networks not to be affected by unfair information or not contain user information.It should be noted that poor design of the censoring procedure significantly reduces the utility of data. For example, the output of random
mapping f rand apparently has no information about S, but it also gives no information about target Y . Alternatively, as a more realistic
example, a neural network with limited capacity possibly acquires less information about S, but it may also result in poorer performance. Therefore, the primary goal of censoring
representation is to obtain an encoder E that reduces information about S, while maintaining information about Y . Formally, the task can be written as a joint
optimization problem of the loss: DISPLAYFORM0 where X indicates the input random variable, E is an encoder that transforms X to representation R, λ is the weighting parameter, and V and L are loss functions that represent how much information about S and Y is present, respectively. Note that S can be any form of variables such
as binary variable, categorical variable, or continuous variable. In this paper, we primarily consider a particular
variant of censoring representation tasks, where we learn E with deep neural networks and S is the user ID (anonymization tasks).
This study proposed MARS, which incorporates multiple adversaries where each adversary has a different role and conducted empirical validations on the efficacy of the proposed method for censoring representations, specifically user-anonymization for the data of wearables.
TAB0 compares the proposed method and several baselines and shows the efficacy of the proposed method against various evaluators.
Figure 2 qualitatively shows that the proposed method provides wellanonymized representations.
FIG4 -c shows that each adversary in MARS has the diverse role, resulting MARS more robust to the update of E as a whole.
All these results support that the proposed method is more effective in removing the influence of a specific factor (user in experiments) compared to the previous methods.One of the reasons why MARS works well is that the adversary is designed to have diverse-views by incorporating random subspace methods, resulting the encoder need to be stronger to deceive the adversary.
It is worth mentioning that the capacity or accuracy of the adversary is not the only a definitive factor that determines the success of the adversarial feature learning, as shown by the superior performance of MARS over MA that has 1 1−α times the larger capacity of MARS.
Moreover, the final performance of AFL is significantly different even if the accuracy of D is reasonably similar during training, as shown in FIG4 -b.
As mentioned in the related work section, such knowledge is essential to design the adversary in practice, and prior studies of adversarial feature learning did not address this issues.Although this paper focused on the case where the subsets are randomly selected and fixed, this might not be necessary.
One of the possible extensions is to determine subsets with more sophisticated ways (e.g., by performing clustering or soft-clustering on the representation spaces after few training iterations), or to learn how to select the subset itself by adding the criterion regarding the diversity of adversaries.
Also, it might be possible to realize the diversity of adversaries by methods other than subspace selection.
One possible way is to constrain weights of two adversaries so that they are an orthogonal view, which is used in semi-supervised learning using co-training BID11 , or it might be worth a try to add different noises for each adversary.It might be worth mentioning about applicability and implications of MARS for other applications of adversarial training, such as image generation.
From the perspective of the applicability, the MARS itself does not rely on any domain-specific settings and is therefore general enough for many applications based on adversarial training.
For example, we can build multiple-adversaries upon the subset of feature spaces (maybe not on the image spaces).
This makes discriminator have diverse-view, so it might be useful for preventing mode collapse that is one of the well-known problems in imagegeneration with adversarial training.
In the context of image-generation, Generative Multi Adversarial Networks proposed by BID0 , which also use multiple adversaries, shows that multiple adversaries are useful for generating better images, and for avoiding mode collapse.
It might be interesting to see if enhancing the diversity of discriminators by preparing asymmetric adversaries as with this paper helps to generate a better image or to avoid mode collapse better.
Table 2 shows the selected λ for each combination of datasets and baselines.
Although the best hyper-parameter might be determined by the balance between log q M and log q D , here we cannot see the obvious relationships between the best λ and the easiness of tasks.

assistant: text: This paper improves the quality of the recently proposed adversarial feature leaning (AFL) approach for incorporating explicit constrains to representations, by introducing the concept of the {\em vulnerableness} of the adversary.