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cs.LG2023
Can we Agree? On the Rashōmon Effect and the Reliability of Post-Hoc Explainable AI
Clement Poiret, Antoine Grigis, Justin Thomas +1
The Rashōmon effect poses challenges for deriving reliable knowledge from machine learning models. This study examined the influence of sample size on explanations from models in a…
cs.CV2023
SepVAE: a contrastive VAE to separate pathological patterns from healthy ones
Robin Louiset, Edouard Duchesnay, Antoine Grigis +2
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.,…