Publications associées à Google DeepMind

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

Benefiting from Multitask Learning to Improve Single Image Super-Resolution

Jean-Philippe Thiran, Claudiu-Cristian Musat, Hazim Kemal Ekenel, Mohammad Saeed Rad

Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem. Recent works on single image super resolution (SISR) are ...

2020

Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks

Sébastien Marcel, Guillaume Heusch, Zohreh Mostaani

This paper addresses the problem of face presentation attack detection using different image modalities. In particular, the usage of short wave infrared (SWIR) imaging is considered. Face presentation attack detection is performed using recent models based ...

IEEE2020

Single-Stage 6D Object Pose Estimation

Pascal Fua, Mathieu Salzmann, Wei Wang, Yinlin Hu

Most recent 6D pose estimation frameworks first rely on a deep network to establish correspondences between 3D object keypoints and 2D image locations and then use a variant of a RANSAC-based Perspective-n-Point (PnP) algorithm. This two-stage process, how ...

2020

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Matthias Grossglauser, Arnout Jan J Devos, Carlos Roberto Medina Temme

Recent advances in transfer learning and few-shot learning largely rely on annotated data related to the goal task during (pre-)training. However, collecting sufficiently similar and annotated data is often infeasible. Building on advances in self-supervis ...

2020

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

Florent Gérard Krzakala, Julien Marcel Daniel Emmanuel Launay

Despite being the workhorse of deep learning, the backpropagation algorithm is no panacea. It enforces sequential layer updates, thus preventing efficient parallelization of the training process. Furthermore, its biological plausibility is being challenged ...

Curran Associates, Inc.2020

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

Matthieu Jean-Michel Philippe Broisin, Jonathan Roth

Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluati ...

WILEY2019

Design of an Always-On Deep Neural Network-Based 1-mu W Voice Activity Detector Aided With a Customized Software Model for Analog Feature Extraction

Minhao Yang

This paper presents an ultra-low-power voice activity detector (VAD). It uses analog signal processing for acoustic feature extraction (AFE) directly on the microphone output, approximate event-driven analog-to-digital conversion (ED-ADC), and digital deep ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

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Google DeepMind

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Benefiting from Multitask Learning to Improve Single Image Super-Resolution

Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks

Single-Stage 6D Object Pose Estimation

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

Automated Detection Of Highly Aggregated Neurons In Microscopic Images Of Macaque Brain

Geometric Deep Learning for Volumetric Computational Fluid Dynamics

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

Design of an Always-On Deep Neural Network-Based 1-mu W Voice Activity Detector Aided With a Customized Software Model for Analog Feature Extraction

Graph Chatbot

Chattez avec Graph Search

Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

Single-Stage 6D Object Pose Estimation

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Geometric Deep Learning for Volumetric Computational Fluid Dynamics

Benefiting from Multitask Learning to Improve Single Image Super-Resolution

Design of an Always-On Deep Neural Network-Based 1-mu W Voice Activity Detector Aided With a Customized Software Model for Analog Feature Extraction

Automated Detection Of Highly Aggregated Neurons In Microscopic Images Of Macaque Brain

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images