1 code implementation • 10 Jan 2023 • Thomas Duboudin, Emmanuel Dellandréa, Corentin Abgrall, Gilles Hénaff, Liming Chen
Deep neural networks do not discriminate between spurious and causal patterns, and will only learn the most predictive ones while ignoring the others.
no code implementations • 17 Oct 2022 • Thomas Duboudin, Emmanuel Dellandréa, Corentin Abgrall, Gilles Hénaff, Liming Chen
Indeed, test-time adaptation methods usually have to rely on a limited representation because of the shortcut learning phenomenon: only a subset of the available predictive patterns is learned with standard training.
no code implementations • 13 Sep 2022 • Thomas Cordier, Victor Bouvier, Gilles Hénaff, Céline Hudelot
Machine Learning models are prone to fail when test data are different from training data, a situation often encountered in real applications known as distribution shift.
no code implementations • 20 May 2022 • Rémy Sun, Clément Masson, Gilles Hénaff, Nicolas Thome, Matthieu Cord
Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data.
no code implementations • 15 Jun 2021 • Thomas Duboudin, Emmanuel Dellandréa, Corentin Abgrall, Gilles Hénaff, Liming Chen
Traditional deep learning algorithms often fail to generalize when they are tested outside of the domain of the training data.