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Title: Invariant Feature Learning by Attribute Perception Matching

Paper content:
An adversarial feature learning (AFL) is a powerful framework to learn representations invariant to a nuisance attribute, which uses an adversarial game between a feature extractor and a categorical attribute classifier.
It theoretically sounds in term of it maximize conditional entropy between attribute and representation.
However, as shown in this paper, the AFL often causes unstable behavior that slows down the convergence.
We propose an {\em attribute perception matching} as an alternative approach, based on the reformulation of conditional entropy maximization as {\em pair-wise distribution matching}. Although the naive approach for realizing the pair-wise distribution matching requires the significantly large number of parameters, the proposed method requires the same number of parameters with AFL but has a better convergence property.
Experiments on both toy and real-world dataset prove that our proposed method converges to better invariant representation significantly faster than AFL.
How to learn representations invariant to nuisance attribute a is technical challenges raised in domain generalizaton BID1 BID9 BID4 BID8 , fair classification, privacy-protection BID2 BID5 , and many other area.
Assume that we are given a training dataset made of pairs S = (x i , y i , a i ) DISPLAYFORM0 , where x is an observation, y is a target of x, and a is a corresponding intrinsic attribute of K-way categorical variable A. The goal of invariant representation learning is to obtain an encoder E that reduces information about attribute a while maintaining information about y.An adversarial game between a feature extractor and an attribute classifier, called adversarial feature learning BID11 , is a powerful framework for this purpose.
The key of AFL is to measure the invariance by leveraging the discriminative power of neural network beyond the pre-defined metric such as l 2 distance or maximum mean discrepancy.
That is, if the external network (also referred to as a discriminator) can predict a from z = E(x), AFL regards z to have considerable information about a.
Formally, the AFL solves the following optimization problem: DISPLAYFORM1 where q M and q D is the conditional probability that M and D gives a correct estimation of y and a respectively.
As BID11 explained, this alternating procedure can be regarded as a way to maximize the conditional entropy H(A|Z) = a∈A,z∈Z −p(a, z) log p(a|z), where A and Z is a support set of a and z.
BID11 also showed that the min-max game has an equilibrium, in which E maximize the conditional entropy H(A|Z).
It has been show superior performance in fairclassification, privacy-protection, and domain generalization tasks BID3 BID2 BID11 BID5 , compared to the predifined metric approaches BID12 BID7 BID8 .Despite
the theoretical justifications, the above min-max formulation is suspicious for several practical issues. Namely,
the gradient from the discriminator vanishes if the discriminator sufficiently trained since E[log q D (a|z=E(x))] is small then. Besides
, in mathematical level, it only keeps away representations from the non-desired point where we can predict a label correctly, but not ensure that it approaches the desired invariant point. Please
also refer FIG1 for visualization of the instability.Note that, Generative Adversarial Networks community, which utilize similar formulation to generate realistic images, evade similar issues by the incorporating alternative objectives, such as the Wasserstein distance BID0 . However
, the Wasserstein distance is defined over two distributions and applying to our setting (consisting of multiple distributions) is not trivial.This paper holds the following contributions to the invariant feature learning problem. First,
we empirically show that AFL is suffered from practical issues that significantly slow down the convergence. We then
reformulate the optimization problem of AFL as pair-wise distribution matching and derive parameter practical realization of pairwise distribution matching while inheriting the merit of AFL that leveraging the discriminative power to measure the invariance. It is worth
mentioning that the reformulation enable us to use Wasserstein metric in theory, however, it is still computationally infeasible in practice because a naive way to calculate the Wasserstein distance between all the pair of the distributions requires O(K 2 ) discriminators, where K = |A|, which raise computational issues both in terms of parameter size and forward/backward time. Finally, we
empirically validate the superior performance of our proposed method on both artificial dataset and real-world datasets.2 CONVERGENCE
ISSUES OF AFL FIG1 -(a-e) visualize a behavior of AFL optimization on synthesized data. Each figure
corresponds to the different timestep of the alternating optimization. The dataset
consists of samples from three Gaussian distributions with different means ([sin( DISPLAYFORM2 , for i ∈ 1, 2, 3, respectively) and the same variance, assuming that each distribution corresponds to different attributes. In each figure
, dots represent the data point, color represents the attribute (domain id), and the contour plot represents the discriminator's decision boundary. A float value
on the top of figures is the negative log-likelihood (NLL) of the dataset measured by the discriminator D (the multi-layer perceptron with 100 hidden units followed by a ReLU activation). Similarly, a
float value in parenthesis on the top of figures is an NLL of a post-hoc classifier D eval that have the same architecture as D. To be more specific, we first train the discriminator 100 times with 128 batch size and train D and E iteratively with stochastic gradient descent with learning rate=0.1. FIG1 -(f,g)
shows the gradient vector fields of different time steps for a = blue, where the arrow represents the direction of the gradient, and the norm represents its magnitude. For simplicity
, we only show the vector fields of a = blue, but the observations are quite similar for the other a.The figure reveals two practical issues in AFL optimization. (1) The distribution
alignment is initially quite slow (compare with the behavior of the proposed method shown in Figure 2 ). This is because the
gradient is small when the discriminator correctly distinguishes a a. (2) AFL behavior is
unstable. The distributions somewhat
align after 40 steps (given 0.683 NLL with the post-hoc classifier), but it is catastrophically failed five steps later because the discriminator did not capture the true conditional entropy (implied by the mostly similar counterplot of D) and therefore gave a false gradient as shown in (f) and (g). The intuitive
reason
for
this phenomenon is that AFLs loss essentially tries to pull a distribution apart from the non-desired point, i.e., the point where we can correctly predict the label. The problem of AFL is that
it only keeps away a distribution from the non-desired point, but not ensure it approaches the desired invariant point. After several steps, D starts
to follow the change of the distribution (as shown in FIG1 . The instability of the AFL also
appears in the significant gap between the NLL of the D and D eval . Note that the second issue may
be alleviated if D has a sufficiently large capacity and is trained many times at each iteration. However, this is not a realistic
assumption since it is fair to say that real datasets are more complicated than this toy situations, making it more challenging to find the supremum.
This paper proposes a new approach to incorporating desired invariance to representations learning, based on the observations that the current state-of-the-art AFL has practical issues.
Empirical results on both toy and real-world datasets support the stable performance of the proposed method to learn invariant features and superior performance on domain generalization tasks.A PROOF OF THE THEOREM 1Proof.
Using the Lagrange multiplier method, the derivative of DISPLAYFORM0 is equal to zero for the maximum entropy H(A|Z).
Solving the simultaneous equations, we can say p(a=1|z) = p(a=2|z) = · · · = p(a=K|z) = 1 K for all z ∈ Z when the conditional entropy is maximized, and based on the definition, the conditional entropy become − log DISPLAYFORM1 holds ∀i = j ∈ A and z ∈ Z.

assistant: text: This paper proposes a new approach to incorporating desired invariance to representations learning, based on the observations that the current state-of-the-art AFL has practical issues.