Search Results for author: Daniel Haziza

Found 8 papers, 3 papers with code

Efficient conditioned face animation using frontally-viewed embedding

no code implementations16 Mar 2022 Maxime Oquab, Daniel Haziza, Ludovic Schwartz, Tao Xu, Katayoun Zand, Rui Wang, Peirong Liu, Camille Couprie

As the quality of few shot facial animation from landmarks increases, new applications become possible, such as ultra low bandwidth video chat compression with a high degree of realism.

ROMUL: Scale Adaptative Population Based Training

no code implementations1 Jan 2021 Daniel Haziza, Jérémy Rapin, Gabriel Synnaeve

In most pragmatic settings, data augmentation and regularization are essential, and require hyperparameter search.

Data Augmentation Image Classification +1

Low Bandwidth Video-Chat Compression using Deep Generative Models

no code implementations1 Dec 2020 Maxime Oquab, Pierre Stock, Oran Gafni, Daniel Haziza, Tao Xu, Peizhao Zhang, Onur Celebi, Yana Hasson, Patrick Labatut, Bobo Bose-Kolanu, Thibault Peyronel, Camille Couprie

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 device using facial landmarks extracted at the sender's side and transmitted over the network.

Population Based Training for Data Augmentation and Regularization in Speech Recognition

no code implementations8 Oct 2020 Daniel Haziza, Jérémy Rapin, Gabriel Synnaeve

It compares favorably to a baseline that does not change those hyperparameters over the course of training, with an 8% relative WER improvement.

Data Augmentation speech-recognition +1

Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning

no code implementations30 Jan 2017 Francois Belletti, Daniel Haziza, Gabriel Gomes, Alexandre M. Bayen

This article shows how the recent breakthroughs in Reinforcement Learning (RL) that have enabled robots to learn to play arcade video games, walk or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems.

reinforcement-learning Reinforcement Learning (RL)

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