305 citations · 534 across the 23 of their papers we have counts for
6 papers · 1 filter
Generative Models from the perspective of Continual Learning
Timothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz +2
Which generative model is the most suitable for Continual Learning? This paper aims at evaluating and comparing generative models on disjoint sequential image generation tasks. We…
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
Antonin Raffin, Ashley Hill, René Traoré +3
State representation learning aims at learning compact representations from raw observations in robotics and control applications. Approaches used for this objective are auto-encod…
Marginal Replay vs Conditional Replay for Continual Learning
Timothée Lesort, Alexander Gepperth, Andrei Stoian +1
We present a new replay-based method of continual classification learning that we term "conditional replay" which generates samples and labels together by sampling from a distribut…
Training Discriminative Models to Evaluate Generative Ones
Timothée Lesort, Andrei Stoain, Jean-François Goudou +1
Generative models are known to be difficult to assess. Recent works, especially on generative adversarial networks (GANs), produce good visual samples of varied categories of image…
Exploring to learn visual saliency: The RL-IAC approach
Celine Craye, Timothee Lesort, David Filliat +1
The problem of object localization and recognition on autonomous mobile robots is still an active topic. In this context, we tackle the problem of learning a model of visual salien…
State Representation Learning for Control: An Overview
Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou +1
Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representatio…