Publications by Tatjana Chavdarova

DeepBreath-automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries

Tatjana Chavdarova, Juliane Dervaux, Mary-Anne Hartley, Julien Niklas Heitmann, Daniel Hinjos García, Martin Jaggi, Daniel Mueller, Alexandre Perez

The interpretation of lung auscultation is highly subjective and relies on non-specific nomenclature. Computer-aided analysis has the potential to better standardize and automate evaluation. We used 35.9 hours of auscultation audio from 572 pediatric outpatients to develop DeepBreath : a deep learning model identifying the audible signatures of acute respiratory illness in children. It comprises a convolutional neural network followed by a logistic regression classifier, aggregating estimates on recordings from eight thoracic sites into a single prediction at the patient-level. Patients were either healthy controls (29%) or had one of three acute respiratory illnesses (71%) including pneumonia, wheezing disorders (bronchitis/asthma), and bronchiolitis). To ensure objective estimates on model generalisability, DeepBreath is trained on patients from two countries (Switzerland, Brazil), and results are reported on an internal 5-fold cross-validation as well as externally validated (extval) on three other countries (Senegal, Cameroon, Morocco). DeepBreath differentiated healthy and pathological breathing with an Area Under the Receiver-Operator Characteristic (AUROC) of 0.93 (standard deviation [SD] +/- 0.01 on internal validation). Similarly promising results were obtained for pneumonia (AUROC 0.75 +/- 0.10), wheezing disorders (AUROC 0.91 +/- 0.03), and bronchiolitis (AUROC 0.94 +/- 0.02). Extval AUROCs were 0.89, 0.74, 0.74 and 0.87 respectively. All either matched or were significant improvements on a clinical baseline model using age and respiratory rate. Temporal attention showed clear alignment between model prediction and independently annotated respiratory cycles, providing evidence that DeepBreath extracts physiologically meaningful representations. DeepBreath provides a framework for interpretable deep learning to identify the objective audio signatures of respiratory pathology.

NATURE PORTFOLIO2023

Semantic Perturbations with Normalizing Flows for Improved Generalization

Tatjana Chavdarova, Martin Jaggi, Sebastian Urban Stich

Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often limited to a fixed set of hard-coded transformations. Recently, several works proposed to use generative models for generating semantically meaningful perturbations to train a classifier. However, because accurate encoding and decoding are critical, these methods, which use architectures that approximate the latent-variable inference, remained limited to pilot studies on small datasets. Exploiting the exactly reversible encoder-decoder structure of normalizing flows, we perform on-manifold perturbations in the latent space to define fully unsupervised data augmentations. We demonstrate that such perturbations match the performance of advanced data augmentation techniques-reaching 96.6% test accuracy for CIFAR-10 using ResNet-18 and outperform existing methods, particularly in low data regimes-yielding 10-25% relative improvement of test accuracy from classical training. We find that our latent adversarial perturbations adaptive to the classifier throughout its training are most effective, yielding the first test accuracy improvement results on real-world datasets-CIFAR-10/100-via latent-space perturbations.

IEEE2021

Deep Generative Models and Applications

Tatjana Chavdarova

Over the past few years, there have been fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. The amount of annotated data drastically increased and supervised deep discriminative models exceeded human-level performances in certain object detection tasks. The increasing availability in quantity and complexity of unlabelled data also opens up exciting possibilities for the development of unsupervised learning methods. Among the family of unsupervised methods, deep generative models find numerous applications. Moreover, as real-world applications include high dimensional data, the ability of generative models to automatically learn semantically meaningful subspaces makes their advancement an essential step toward developing more efficient algorithms. Generative Adversarial Networks (GANs) are a family of unsupervised generative algorithms that have demonstrated impressive performance for data synthesis and are now used in a wide range of computer vision tasks. Despite this success, they gained a reputation for being difficult to train, which results in a time-consuming and human-involved development process to use them. In the first part of this thesis, we focus on improving the stability and the performances of GANs. Foremost, we consider an alternative training process to the standard one, named SGAN, in which several adversarial âlocalâ pairs of networks are trained independently so that a âglobalâ supervising pair of networks can be trained against them. Experimental results on both toy and real-world problems demonstrate that this approach outperforms standard training in terms of better mitigating mode collapse, stability while converging and that it surprisingly, increases the convergence speed as well. To further reduce the computational footprint while maintaining the stability and performance advantages of SGAN, we focus on training a single pair of adversarial networks using variance reduced gradient. More precisely, we study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can prevent the convergence of standard game optimization methods, while the batch version converges. We address this issue with two stochastic variance-reduced gradient and extragradient optimization algorithms for GANs, named SVRG-GAN and SVRE, respectively. We observe empirically that SVRE performs similarly to a batch method on the MNIST dataset, while being computationally cheaper, and that SVRE yields more stable GAN training on standard datasets. In the second part of the thesis we present our work on people detection. People detection methods are highly sensitive to occlusions between pedestrians, and using joint visual information from multiple synchronized cameras gives the opportunity to improve detection performance. We address the problem of multi-view people occupancy map estimation using an endâtoâend deep learning algorithm called DeepMCD that jointly utilizes the correlated streams of visual information. DeepMCD empirically outperformed the classical approaches by a large margin. Finally, we present a new large-scale and high-resolution dataset, named WILDTRACK. We provide an accurate joint calibration, as well as a series of benchmark results using baseline algorithms published over the recent months for multi-view detection with deep neural networks, and trajectory estimation using a non-Markovian model.

EPFL2020

Taming GANs with Lookahead

Tatjana Chavdarova, François Fleuret, Martin Jaggi, Matteo Pagliardini

Idiap2020

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We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can prevent the convergence of standard game optimization methods, while the batch version converges. We address this issue with a novel stochastic variance-reduced extragradient (SVRE) optimization algorithm, which for a large class of games improves upon the previous convergence rates proposed in the literature. We observe empirically that SVRE performs similarly to a batch method on MNIST while being computationally cheaper, and that SVRE yields more stable GAN training on standard datasets.

2019

SGAN: An Alternative Training of Generative Adversarial Networks

Tatjana Chavdarova, François Fleuret

The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this success, they gained a reputation for being difficult to train, what results in a time-consuming and human-involved development process to use them. We consider an alternative training process, named SGAN, in which several adversarial "local" pairs of networks are trained independently so that a "global" supervising pair of networks can be trained against them. The goal is to train the global pair with the corresponding ensemble opponent for improved performances in terms of mode coverage. This approach aims at increasing the chances that learning will not stop for the global pair, preventing both to be trapped in an unsatisfactory local minimum, or to face oscillations often observed in practice. To guarantee the latter; the global pair never affects the local ones. The rules of SGAN training are thus as follows: the global generator and discriminator are trained using the local discriminators and generators, respectively, whereas the local networks are trained with their fixed local opponent. Experimental results on both toy and real-world problems demonstrate that this approach outperforms standard training in terms of better mitigating mode collapse, stability while converging and that it surprisingly, increases the convergence speed as well.

IEEE2018

Stochastic Variance Reduced Gradient Optimization of Generative Adversarial Networks

Tatjana Chavdarova, François Fleuret, Martin Jaggi, Sebastian Urban Stich

2018

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People detection methods are highly sensitive to occlusions between pedestrians, which are extremely frequent in many situations where cameras have to be mounted at a limited height. The reduction of camera prices allows for the generalization of static multi-camera set-ups. Using joint visual information from multiple synchronized cameras gives the opportunity to improve detection performance. In this paper, we present a new large-scale and high-resolution dataset. It has been captured with seven static cameras in a public open area, and unscripted dense groups of pedestrians standing and walking. Together with the camera frames, we provide an accurate joint (extrinsic and intrinsic) calibration, as well as 7 series of 400 annotated frames for detection at a rate of 2 frames per second. This results in over 40 000 bounding boxes delimiting every person present in the area of interest, for a total of more than 300 individuals. We provide a series of benchmark results using baseline algorithms published over the recent months for multi-view detection with deep neural networks, and trajectory estimation using a non-Markovian model.

IEEE2018

Deep Multi-Camera People Detection

Tatjana Chavdarova, François Fleuret

2017