Search Results for author: Robin Louiset

Found 5 papers, 5 papers with code

Separating common from salient patterns with Contrastive Representation Learning

1 code implementation19 Feb 2024 Robin Louiset, Edouard Duchesnay, Antoine Grigis, Pietro Gori

Then, we motivate a novel Mutual Information minimization strategy to prevent information leakage between common and salient distributions.

Contrastive Learning Representation Learning

Double InfoGAN for Contrastive Analysis

1 code implementation31 Jan 2024 Florence Carton, Robin Louiset, Pietro Gori

Experimental results on four visual datasets, from simple synthetic examples to complex medical images, show that the proposed method outperforms SOTA CA-VAEs in terms of latent separation and image quality.

SepVAE: a contrastive VAE to separate pathological patterns from healthy ones

1 code implementation12 Jul 2023 Robin Louiset, Edouard Duchesnay, Antoine Grigis, Benoit Dufumier, Pietro Gori

Contrastive Analysis VAE (CA-VAEs) is a family of Variational auto-encoders (VAEs) that aims at separating the common factors of variation between a background dataset (BG) (i. e., healthy subjects) and a target dataset (TG) (i. e., patients) from the ones that only exist in the target dataset.

Integrating Prior Knowledge in Contrastive Learning with Kernel

1 code implementation3 Jun 2022 Benoit Dufumier, Carlo Alberto Barbano, Robin Louiset, Edouard Duchesnay, Pietro Gori

To this end, we use kernel theory to propose a novel loss, called decoupled uniformity, that i) allows the integration of prior knowledge and ii) removes the negative-positive coupling in the original InfoNCE loss.

Contrastive Learning Data Augmentation

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