305 citations · 765 across the 23 of their papers we have counts for
12 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…
Don't forget, there is more than forgetting: new metrics for Continual Learning
Natalia Díaz-Rodríguez, Vincenzo Lomonaco, David Filliat +1
Continual learning consists of algorithms that learn from a stream of data/tasks continuously and adaptively thought time, enabling the incremental development of ever more complex…
Learning structure-from-motion from motion
Clément Pinard, Laure Chevalley, Antoine Manzanera +1
This work is based on a questioning of the quality metrics used by deep neural networks performing depth prediction from a single image, and then of the usability of recently publi…
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…
Continual State Representation Learning for Reinforcement Learning using Generative Replay
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
We consider the problem of building a state representation model in a continual fashion. As the environment changes, the aim is to efficiently compress the sensory state's informat…
Multi range Real-time depth inference from a monocular stabilized footage using a Fully Convolutional Neural Network
Clément Pinard, Laure Chevalley, Antoine Manzanera +1
Using a neural network architecture for depth map inference from monocular stabilized videos with application to UAV videos in rigid scenes, we propose a multi-range architecture f…
End-to-end depth from motion with stabilized monocular videos
Clément Pinard, Laure Chevalley, Antoine Manzanera +1
We propose a depth map inference system from monocular videos based on a novel dataset for navigation that mimics aerial footage from gimbal stabilized monocular camera in rigid sc…
Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning
Hugo Caselles-Dupré, Louis Annabi, Oksana Hagen +2
Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents. It is of lower complexity compared to similar 3D platforms (e.g. De…
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…