25 citations · 116 across the 31 of their papers we have counts for
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Optimal Budgeted Rejection Sampling for Generative Models
Alexandre Verine, Muni Sreenivas Pydi, Benjamin Negrevergne +1
Rejection sampling methods have recently been proposed to improve the performance of discriminator-based generative models. However, these methods are only optimal under an unlimit…
Adversarial attacks for mixtures of classifiers
Lucas Gnecco Heredia, Benjamin Negrevergne, Yann Chevaleyre
Mixtures of classifiers (a.k.a. randomized ensembles) have been proposed as a way to improve robustness against adversarial attacks. However, it has been shown that existing attack…
Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows
Alexandre Verine, Benjamin Negrevergne, Muni Sreenivas Pydi +1
Achieving a balance between image quality (precision) and diversity (recall) is a significant challenge in the domain of generative models. Current state-of-the-art models primaril…
Training Normalizing Flows with the Precision-Recall Divergence
Alexandre Verine, Benjamin Negrevergne, Muni Sreenivas Pydi +1
Generative models can have distinct mode of failures like mode dropping and low quality samples, which cannot be captured by a single scalar metric. To address this, recent works p…
On the Role of Randomization in Adversarially Robust Classification
Lucas Gnecco-Heredia, Yann Chevaleyre, Benjamin Negrevergne +2
Deep neural networks are known to be vulnerable to small adversarial perturbations in test data. To defend against adversarial attacks, probabilistic classifiers have been proposed…