Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

Advances and Open Problems in Federated Learning

Facial expression recognition has been an active research area over the past few decades, and it is still challenging due to the high intra-class variation. 
Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG and LBP, followed by a classifier trained on a database of images or videos. 
Most of these works perform reasonably well on datasets of images captured in a controlled condition, but fail to perform as good on more challenging datasets with more image variation and partial faces. 
In recent years, several works proposed an end-to-end framework for facial expression recognition, using deep learning models. 
Despite the better performance of these works, there still seems to be a great room for improvement. 
In this work, we propose a deep learning approach based on attentional convolutional network, which is able to focus on important parts of the face, and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. 
We also use a visualization technique which is able to find important face regions for detecting different emotions, based on the classifier's output. 
Through experimental results, we show that different emotions seems to be sensitive to different parts of the face.

Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face.

Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network.

Deep image translation methods have recently shown excellent results, outputting high-quality images covering multiple modes of the data distribution. There has also been increased interest in disentangling the internal representations learned by deep methods to further improve their performance and achieve a finer control. In this paper, we bridge these two objectives and introduce the concept of cross-domain disentanglement. We aim to separate the internal representation into three parts. The shared part contains information for both domains. The exclusive parts, on the other hand, contain only factors of variation that are particular to each domain. We achieve this through bidirectional image translation based on Generative Adversarial Networks and cross-domain autoencoders, a novel network component. Our model offers multiple advantages. We can output diverse samples covering multiple modes of the distributions of both domains, perform domain- specific image transfer and interpolation, and cross-domain retrieval without the need of labeled data, only paired images. We compare our model to the state-of-the-art in multi-modal image translation and achieve better results for translation on challenging datasets as well as for cross-domain retrieval on realistic datasets.

Image-to-image translation for cross-domain disentanglement

Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy–enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy–related research in the area of biometrics and review existing work on Biometric Privacy–Enhancing Techniques (B–PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B–PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future.

/pdf/privacy-enhancing-face-biometrics-a-comprehensive-survey-2wfk78ga6m.pdf

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations. This paper investigates anonymization methods based on representation learning and deep neural networks, and motivated by novel information theoretical bounds. We introduce a novel training objective for simultaneously training a predictor over target variables of interest (the regular labels) while preventing an intermediate representation to be predictive of the private labels. The architecture is based on three sub-networks: one going from input to representation, one from representation to predicted regular labels, and one from representation to predicted private labels. The training procedure aims at learning representations that preserve the relevant part of the information (about regular labels) while dismissing information about the private labels which correspond to the identity of a person. We demonstrate the success of this approach for two distinct classification versus anonymization tasks (handwritten digits and sentiment analysis).

pdf/learning-anonymized-representations-with-adversarial-neural-h4dyowy1em.pdf

Learning Anonymized Representations with Adversarial Neural Networks

In the context of the Neyman-Rubin framework with binary treatment and outcome, we devise and conduct a rich benchmark study whose general ambition is to ﬁnd the best practices to achieve good predictions of the CATE with machine learning techniques. The major part of our work revolves around two questions: i) what is the most reliable metric to select the best model among several competitors? We experimentally challenge the AUUC with the modiﬁed MSE associated with the AIPW pseudo-outcome, to which we introduce an alternative formulation. We compare them to multiple ground-truth-based metrics on synthetic datasets. ii) how do data generation processes impact the performance of models? We design a special structure for the benchmark and introduce axes of analysis to explore the global and local behaviours of several models (essentially neural nets and random forests). Beyond the validity of our conclusions, another important ambition of our work is to provide valuable data and methods to be included in the demanding and uniﬁed standard of model assessment we need to adopt as a research community in order to deepen our understanding of the CATE task and improve our models.

An in-depth benchmark study of the CATE estimation problem: experimental framework, metrics and models Version 1

Many Machine Learning algorithms aiming at classifying signals/images $X$ among a number of discrete labels $Y$ involve training instances, from which the predictor $P_{Y\vert X}$ is extracted according to the data distribution $P_{X\vert Y}$ . This predictor is later used to predict the appropriate label for other instances of $X$ that are hence assumed to be drawn from the same distribution. This is a fundamental requirement for many realworld applications, therefore it is of great importance to monitor the reliability of the classification provided by the algorithm based on the learned distributions, when the test set statistics differ from the training set ones. This paper makes a step in that direction by proposing a Black Box Shift Detector of the data evolution (covariate shift). ‘Black Box’ here means that it does not require any knowledge of the predictor's architecture. Experiments demonstrate accurate detection on different high-dimensional datasets of natural images.

https://hal-centralesupelec.archives-ouvertes.fr/hal-02172275/document

Detecting Covariate Shift with Black Box Predictors

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Learning Anonymized Representations with Adversarial Neural Networks

An in-depth benchmark study of the CATE estimation problem: experimental framework, metrics and models Version 1

Detecting Covariate Shift with Black Box Predictors