5 papers
Swapping Semantic Contents for Mixing Images
Rémy Sun, Clément Masson, Gilles Hénaff +2
Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data. In fact, the amount of available labeled data has become the principal bot…
Towards efficient feature sharing in MIMO architectures
Rémy Sun, Alexandre Ramé, Clément Masson +2
Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free…
MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks
Alexandre Rame, Remy Sun, Matthieu Cord
Recent strategies achieved ensembling "for free" by fitting concurrently diverse subnetworks inside a single base network. The main idea during training is that each subnetwork lea…
Counterfactuals uncover the modular structure of deep generative models
Michel Besserve, Arash Mehrjou, Rémy Sun +1
Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. However, manipulating such representation to…
KS(conf ): A Light-Weight Test if a ConvNet Operates Outside of Its Specifications
Rémy Sun, Christoph H. Lampert
Computer vision systems for automatic image categorization have become accurate and reliable enough that they can run continuously for days or even years as components of real-worl…