1 citations · 1 across the 3 of their papers we have counts for
4 papers
Look Beyond Bias with Entropic Adversarial Data Augmentation
Thomas Duboudin, Emmanuel Dellandréa, Corentin Abgrall +2
Deep neural networks do not discriminate between spurious and causal patterns, and will only learn the most predictive ones while ignoring the others. This shortcut learning behavi…
Learning Less Generalizable Patterns with an Asymmetrically Trained Double Classifier for Better Test-Time Adaptation
Thomas Duboudin, Emmanuel Dellandréa, Corentin Abgrall +2
Deep neural networks often fail to generalize outside of their training distribution, in particular when only a single data domain is available during training. While test-time ada…
Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalization
Thomas Duboudin, Emmanuel Dellandréa, Corentin Abgrall +2
Traditional deep learning algorithms often fail to generalize when they are tested outside of the domain of the training data. The issue can be mitigated by using unlabeled data fr…
Toward a Procedural Fruit Tree Rendering Framework for Image Analysis
Thomas Duboudin, Maxime Petit, Liming Chen
We propose a procedural fruit tree rendering framework, based on Blender and Python scripts allowing to generate quickly labeled dataset (i.e. including ground truth semantic segme…