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pro vyhledávání: '"Louiset, Robin"'
Contrastive Analysis is a sub-field of Representation Learning that aims at separating common factors of variation between two datasets, a background (i.e., healthy subjects) and a target (i.e., diseased subjects), from the salient factors of variati
Externí odkaz:
http://arxiv.org/abs/2402.11928
Contrastive Analysis (CA) deals with the discovery of what is common and what is distinctive of a target domain compared to a background one. This is of great interest in many applications, such as medical imaging. Current state-of-the-art (SOTA) met
Externí odkaz:
http://arxiv.org/abs/2401.17776
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
Externí odkaz:
http://arxiv.org/abs/2307.06206
Data augmentation is a crucial component in unsupervised contrastive learning (CL). It determines how positive samples are defined and, ultimately, the quality of the learned representation. In this work, we open the door to new perspectives for CL b
Externí odkaz:
http://arxiv.org/abs/2206.01646
Autor:
Dufumier, Benoit, Gori, Pietro, Petiton, Sara, Louiset, Robin, Mangin, Jean-François, Grigis, Antoine, Duchesnay, Edouard
Publikováno v:
In NeuroImage 1 August 2024 296
Autor:
Louiset, Robin, Gori, Pietro, Dufumier, Benoit, Houenou, Josselin, Grigis, Antoine, Duchesnay, Edouard
Publikováno v:
ECML/PKDD 2021
Subtype Discovery consists in finding interpretable and consistent sub-parts of a dataset, which are also relevant to a certain supervised task. From a mathematical point of view, this can be defined as a clustering task driven by supervised learning
Externí odkaz:
http://arxiv.org/abs/2107.01988