Publications associées à Réseaux antagonistes génératifs

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Sabine Süsstrunk, Majed El Helou, Frederike Dümbgen

In this supplementary material, we present the details of the neural network architecture and training settings used in all our experiments. This holds for all experiments presented in the main paper as well as in this supplementary material. We also show ...

2020

Efficient Proximal Mapping of the 1-path-norm of Shallow Networks

Volkan Cevher, Paul Thierry Yves Rolland, Fabian Ricardo Latorre Gomez, Shaul Nadav Hallak

We demonstrate two new important properties of the 1-path-norm of shallow neural networks. First, despite its non-smoothness and non-convexity it allows a closed form proximal operator which can be efficiently computed, allowing the use of stochastic proxi ...

2020

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

A large body of research has focused on adversarial attacks which require to modify all input features with small l2- or l∞-norms. In this paper we instead focus on query-efficient sparse attacks in the black-box setting. Our versatile framework, Sparse-RS ...

2020

Contextual semantic interpretability

Devis Tuia, Sylvain Lobry, Nicolas Courty

Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in t ...

LNCS2020

XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera

Pascal Fua, Helge Jochen Rhodin, Gerard Pons Moll

We present a real-time approach for multi-person 3D motion capture at over 30 fps using a single RGB camera. It operates successfully in generic scenes which may contain occlusions by objects and by other people. Our method operates in subsequent stages. T ...

ASSOC COMPUTING MACHINERY2020

On the Relationship between Self-Attention and Convolutional Layers

Martin Jaggi, Andreas Loukas, Jean-Baptiste Francis Marie Juliette Cordonnier

Recent trends of incorporating attention mechanisms in vision have led re- searchers to reconsider the supremacy of convolutional layers as a primary build- ing block. Beyond helping CNNs to handle long-range dependencies, Ramachandran et al. (2019) showed ...

2020

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

François Fleuret, Nikolaos Pappas, Angelos Katharopoulos, Apoorv Vyas

Transformers achieve remarkable performance in several tasks but due to their quadratic complexity, with respect to the input’s length, they are prohibitively slow for very long sequences. To address this limitation, we express the self-attention as a line ...

Idiap2020

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Yu-Ting Huang, Kamalaruban Parameswaran, Cheng Shi

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampli ...

2020

Multi-View Shape Estimation of Transparent Containers

Pascal Frossard, Andrea Cavallaro, Apostolos Modas

The 3D localisation of an object and the estimation of its properties, such as shape and dimensions, are challenging under varying degrees of transparency and lighting conditions. In this paper, we propose a method for jointly localising container-like obj ...

2020

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Réseaux antagonistes génératifs

Stability of time-sensitive networks: strategies for partial regulation

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Efficient Proximal Mapping of the 1-path-norm of Shallow Networks

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Contextual semantic interpretability

XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera

On the Relationship between Self-Attention and Convolutional Layers

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Multi-View Shape Estimation of Transparent Containers

Graph Chatbot

Chattez avec Graph Search

XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Stability of time-sensitive networks: strategies for partial regulation

Contextual semantic interpretability

On the Relationship between Self-Attention and Convolutional Layers

Efficient Proximal Mapping of the 1-path-norm of Shallow Networks

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Multi-View Shape Estimation of Transparent Containers

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics