Masksembles for Uncertainty Estimation

Deep neural networks have amply demonstrated their prowess but estimating the reliability of their predictions remains challenging. Deep Ensembles are widely considered as being one of the best methods for generating uncertainty estimates but are very expensive to train and evaluate. MC-Dropout is another popular alternative, which is less expensive, but also less reliable. Our central intuition is that there is a continuous spectrum of ensemble-like models of which MC-Dropout and Deep Ensembles are extreme examples. The first uses effectively infinite number of highly correlated models while the second relies on a finite number of independent models. To combine the benefits of both, we introduce Masksembles. Instead of randomly dropping parts of the network as in MC-dropout, Masksemble relies on a fixed number of binary masks, which are parameterized in a way that allows to change correlations between individual models. Namely, by controlling the overlap between the masks and their density one can choose the optimal configuration for the task at hand. This leads to a simple and easy to implement method with performance on par with Ensembles at a fraction of the cost. We experimentally validate Masksembles on two widely used datasets, CIFAR10 and ImageNet.

Robustness and invariance properties of image classifiers

Apostolos Modas

Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is important to ensure that their decisions remain correct when deployed in noisy environments. In fact, deep networks are not robust to a large variety of semantic-preserving image modifications, even to imperceptible image changes -- known as adversarial perturbations -- that can arbitrarily flip the prediction of a classifier. The poor robustness of image classifiers to small data distribution shifts raises serious concerns regarding their trustworthiness. To build reliable machine learning models, we must design principled methods to analyze and understand the mechanisms that shape robustness and invariance. This is exactly the focus of this thesis.First, we study the problem of computing sparse adversarial perturbations, and exploit the geometry of the decision boundaries of image classifiers for computing sparse perturbations very fast. We evaluate the robustness of deep networks to sparse adversarial perturbations in high-dimensional datasets, and reveal a qualitative correlation between the location of the perturbed pixels and the semantic features of the images. Such correlation suggests a deep connection between adversarial examples and the data features that image classifiers learn.To better understand this connection, we provide a geometric framework that connects the distance of data samples to the decision boundary, with the features existing in the data. We show that deep classifiers have a strong inductive bias towards invariance to non-discriminative features, and that adversarial training exploits this property to confer robustness. We demonstrate that the invariances of robust classifiers are useful in data-scarce domains, while the improved understanding of the data influence on the inductive bias of deep networks can be exploited to design more robust classifiers. Finally, we focus on the challenging problem of generalization to unforeseen corruptions of the data, and we propose a novel data augmentation scheme that relies on simple families of max-entropy image transformations to confer robustness to common corruptions. We analyze our method and demonstrate the importance of the mixing strategy on synthesizing corrupted images, and we reveal the robustness-accuracy trade-offs arising in the context of common corruptions. The controllable nature of our method permits to easily adapt it to other tasks and achieve robustness to distribution shifts in data-scarce applications.Overall, our results contribute to the understanding of the fundamental mechanisms of deep image classifiers, and pave the way for building more reliable machine learning systems that can be deployed in real-world environments.

EPFL2022

Sparse and Low-rank Modeling for Automatic Speech Recognition

Pranay Dighe

This thesis deals with exploiting the low-dimensional multi-subspace structure of speech towards the goal of improving acoustic modeling for automatic speech recognition (ASR). Leveraging the parsimonious hierarchical nature of speech, we hypothesize that whenever a speech signal is measured in a high-dimensional feature space, the true class information is embedded in low-dimensional subspaces whereas noise is scattered as random high-dimensional erroneous estimations in the features. In this context, the contribution of this thesis is twofold: (i) identify sparse and low-rank modeling approaches as excellent tools for extracting the class-specific low-dimensional subspaces in speech features, and (ii) employ these tools under novel ASR frameworks to enrich the acoustic information present in the speech features towards the goal of improving ASR. Techniques developed in this thesis focus on deep neural network (DNN) based posterior features which, under the sparse and low-rank modeling approaches, unveil the underlying class-specific low-dimensional subspaces very elegantly. In this thesis, we tackle ASR tasks of varying difficulty, ranging from isolated word recognition (IWR) and connected digit recognition (CDR) to large-vocabulary continuous speech recognition (LVCSR). For IWR and CDR, we propose a novel \textit{Compressive Sensing} (CS) perspective towards ASR. Here exemplar-based speech recognition is posed as a problem of recovering sparse high-dimensional word representations from compressed low-dimensional phonetic representations. In the context of LVCSR, this thesis argues that albeit their power in representation learning, DNN based acoustic models still have room for improvement in exploiting the \textit{union of low-dimensional subspaces} structure of speech data. Therefore, this thesis proposes to enhance DNN posteriors by projecting them onto the manifolds of the underlying classes using principal component analysis (PCA) or compressive sensing based dictionaries. Projected posteriors are shown to be more accurate training targets for learning better acoustic models, resulting in improved ASR performance. The proposed approach is evaluated on both close-talk and far-field conditions, confirming the importance of sparse and low-rank modeling of speech in building a robust ASR framework. Finally, the conclusions of this thesis are further consolidated by an information theoretic analysis approach which explicitly quantifies the contribution of proposed techniques in improving ASR.

EPFL2019

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Paul Thierry Yves Rolland

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same environment. However, the real world is most of the time not static, and the environment in which the model will be used can differ from the one in which it is trained. It is hence desirable to design models that are robust to changes of environments. This encapsulates a large family of topics in ML, such as adversarial robustness, meta-learning, domain adaptation and others, depending on the way the environment is perturbed.In this dissertation, we focus on methods for training models whose performance does not drastically degrade when applied to environments differing from the one the model has been trained in. Various types of environmental changes will be treated, differing in their structure or magnitude. Each setup defines a certain kind of robustness to certain environmental changes, and leads to a certain optimization problem to be solved. We consider 3 different setups, and propose algorithms for solving each associated problem using 3 different types of methods, namely, min-max optimization (Chapter 2), regularization (Chapter 3) and variable selection (Chapter 4).Leveraging the framework of distributionally robust optimization, which phrases the problem of robust training as a min-max optimization problem, we first aim to train robust models by directly solving the associated min-max problem. This is done by exploiting recent work on game theory as well as first-order sampling algorithms based on Langevin dynamics. Using this approach, we propose a method for training robust agents in the scope of Reinforcement Learning.We then treat the case of adversarial robustness, i.e., robustness to small arbitrary perturbation of the model's input. It is known that neural networks trained using classical optimization methods are particularly sensitive to this type of perturbations. The adversarial robustness of a model is tightly connected to its smoothness, which is quantified by its so-called Lipschitz constant. This constant measures how much the model's output changes upon any bounded input perturbation. We hence develop a method to estimate an upper bound on the Lipschitz constant of neural networks via polynomial optimization, which can serve as a robustness certificate against adversarial attacks. We then propose to penalize the Lipschitz constant during training by minimizing the 1-path-norm of the neural network, and we develop an algorithm for solving the resulting regularized problem by efficiently computing the proximal operator of the 1-path-norm term, which is non-smooth and non-convex.Finally, we consider a scenario where the environmental changes can be arbitrary large (as opposed to adversarial robustness), but need to preserve a certain causal structure. Recent works have demonstrated interesting connections between robustness and the use of causal variables. Assuming that certain mechanisms remain invariant under some change of the environment, it has been shown that knowing the underlying causal structure of the data at hand allows to train models that are invariant to such changes. Unfortunately, in many cases, the causal structure is unknown. We thus propose a causal discovery algorithm from observational data in the case of non-linear additive model.

EPFL2022

Robustness and invariance properties of image classifiers

Apostolos Modas

EPFL2022

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Paul Thierry Yves Rolland

EPFL2022

Sparse and Low-rank Modeling for Automatic Speech Recognition

Pranay Dighe

EPFL2019