3 citations · 6 across the 2 of their papers we have counts for
6 papers
Defending against Reconstruction Attacks with Rényi Differential Privacy
Pierre Stock, Igor Shilov, Ilya Mironov +1
Reconstruction attacks allow an adversary to regenerate data samples of the training set using access to only a trained model. It has been recently shown that simple heuristics can…
LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
Ben Graham, Alaaeldin El-Nouby, Hugo Touvron +4
We design a family of image classification architectures that optimize the trade-off between accuracy and efficiency in a high-speed regime. Our work exploits recent findings in at…
Low Bandwidth Video-Chat Compression using Deep Generative Models
Maxime Oquab, Pierre Stock, Oran Gafni +9
To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's de…
Training with Quantization Noise for Extreme Model Compression
Angela Fan, Pierre Stock, Benjamin Graham +4
We tackle the problem of producing compact models, maximizing their accuracy for a given model size. A standard solution is to train networks with Quantization Aware Training, wher…
And the Bit Goes Down: Revisiting the Quantization of Neural Networks
Pierre Stock, Armand Joulin, Rémi Gribonval +2
In this paper, we address the problem of reducing the memory footprint of convolutional network architectures. We introduce a vector quantization method that aims at preserving the…
Equi-normalization of Neural Networks
Pierre Stock, Benjamin Graham, Rémi Gribonval +1
Modern neural networks are over-parametrized. In particular, each rectified linear hidden unit can be modified by a multiplicative factor by adjusting input and output weights, wit…