One of the pursued objectives of deep learning is to provide tools that learn
abstract representations of reality from the observation of multiple contextual
situations. More precisely, one wishes to extract disentangled representations
which are (i) low dimensional and (ii) whose components are independent and
correspond to concepts capturing the essence of the objects under consideration
(Locatello et al., 2019b). One step towards this ambitious project consists in
learning disentangled representations with respect to a predefined (sensitive)
attribute, e.g., the gender or age of the writer. Perhaps one of the main
application for such disentangled representations is fair classification.
Existing methods extract the last layer of a neural network trained with a loss
that is composed of a cross-entropy objective and a disentanglement
regularizer. In this work, we adopt an information-theoretic view of this
problem which motivates a novel family of regularizers that minimizes the
mutual information between the latent representation and the sensitive
attribute conditional to the target. The resulting set of losses, called
CLINIC, is parameter free and thus, it is easier and faster to train. CLINIC
losses are studied through extensive numerical experiments by training over 2k
neural networks. We demonstrate that our methods offer a better
disentanglement/accuracy trade-off than previous techniques, and generalize
better than training with cross-entropy loss solely provided that the
disentanglement task is not too constraining.

从多种上下文环境的观察中提取现实的抽象表示是深度学习的一个追求目标之一。本文采用信息论的视角来研究这个问题，提出了一种新颖的正则化方法，称为 CLINIC 损失，通过大量的数值实验证明了这种方法在解缠表征与准确性之间取得更好的平衡，并且在不过于限制解缠任务的情况下具有更好的普适性。

用一种新颖的信息论目标分解表示以实现公平分类

A Novel Information-Theoretic Objective to Disentangle Representations  for Fair Classification

A fundamental question in theoretical machine learning is generalization.
Over the past decades, the PAC-Bayesian approach has been established as a
flexible framework to address the generalization capabilities of machine
learning algorithms, and design new ones. Recently, it has garnered increased
interest due to its potential applicability for a variety of learning
algorithms, including deep neural networks. In parallel, an
information-theoretic view of generalization has developed, wherein the
relation between generalization and various information measures has been
established. This framework is intimately connected to the PAC-Bayesian
approach, and a number of results have been independently discovered in both
strands. In this monograph, we highlight this strong connection and present a
unified treatment of generalization. We present techniques and results that the
two perspectives have in common, and discuss the approaches and interpretations
that differ. In particular, we demonstrate how many proofs in the area share a
modular structure, through which the underlying ideas can be intuited. We pay
special attention to the conditional mutual information (CMI) framework;
analytical studies of the information complexity of learning algorithms; and
the application of the proposed methods to deep learning. This monograph is
intended to provide a comprehensive introduction to information-theoretic
generalization bounds and their connection to PAC-Bayes, serving as a
foundation from which the most recent developments are accessible. It is aimed
broadly towards researchers with an interest in generalization and theoretical
machine learning.

该研究旨在提供信息论概括界限及其与 PAC-Bayes 的关联的全面介绍，为最近的发展提供基础，广泛面向对概括和理论机器学习感兴趣的研究人员。