97 citations · 136 across the 10 of their papers we have counts for
12 papers
Diffusion bridges vector quantized Variational AutoEncoders
Max Cohen, Guillaume Quispe, Sylvain Le Corff +2
Vector Quantized-Variational AutoEncoders (VQ-VAE) are generative models based on discrete latent representations of the data, where inputs are mapped to a finite set of learned em…
Learning Natural Language Generation from Scratch
Alice Martin Donati, Guillaume Quispe, Charles Ollion +3
This paper introduces TRUncated ReinForcement Learning for Language (TrufLL), an original ap-proach to train conditional language models from scratch by only using reinforcement le…
NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform
Achille Thin, Yazid Janati, Sylvain Le Corff +5
Sampling from a complex distribution and approximating its intractable normalizing constant Z are challenging problems. In this paper, a novel family of importance samplers (IS…
Joint self-supervised blind denoising and noise estimation
Jean Ollion, Charles Ollion, Elisabeth Gassiat +2
We propose a novel self-supervised image blind denoising approach in which two neural networks jointly predict the clean signal and infer the noise distribution. Assuming that the…
CoRe: Color Regression for Multicolor Fashion Garments
Alexandre Rame, Arthur Douillard, Charles Ollion
Developing deep networks that analyze fashion garments has many real-world applications. Among all fashion attributes, color is one of the most important yet challenging to detect.…
The Monte Carlo Transformer: a stochastic self-attention model for sequence prediction
Alice Martin, Charles Ollion, Florian Strub +2
This paper introduces the Sequential Monte Carlo Transformer, an original approach that naturally captures the observations distribution in a transformer architecture. The keys, qu…
Insights from the Future for Continual Learning
Arthur Douillard, Eduardo Valle, Charles Ollion +2
Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting. In this…
PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning
Arthur Douillard, Matthieu Cord, Charles Ollion +2
Lifelong learning has attracted much attention, but existing works still struggle to fight catastrophic forgetting and accumulate knowledge over long stretches of incremental learn…
DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine
Jean Ollion, Charles Ollion
The mother machine is a popular microfluidic device that allows long-term time-lapse imaging of thousands of cells in parallel by microscopy. It has become a valuable tool for sing…
OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation
Alexandre Rame, Emilien Garreau, Hedi Ben-Younes +1
Object detectors tend to perform poorly in new or open domains, and require exhaustive yet costly annotations from fully labeled datasets. We aim at benefiting from several dataset…
Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction
Charles Corbière, Hedi Ben-Younes, Alexandre Ramé +1
In this paper, we present a method to learn a visual representation adapted for e-commerce products. Based on weakly supervised learning, our model learns from noisy datasets crawl…
Dynamics and Performance of Susceptibility Propagation on Synthetic Data
Erik Aurell, Charles Ollion, Yasser Roudi
We study the performance and convergence properties of the Susceptibility Propagation (SusP) algorithm for solving the Inverse Ising problem. We first study how the temperature par…