Content Manipulation

Semantic augmentation by mixing contents for semi-supervised learning
Leveraging unlabeled examples is a crucial issue for boosting performances in semi-supervised learning. In this work, we introduce the …
Remy Sun, Clément Masson, Gilles Hénaff, Nicolas Thome, Matthieu Cord
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Semantic augmentation by mixing contents for semi-supervised learning
Editing sample contents for data augmentation
While Mixing Samples Data Augmentation (MSDA) offer a powerful tool for regularization, they offer very little control on the content mixed. I study how manipulating the contents mixed into the final augmented image can improve the training of networks.
Reconciling feature sharing and multiple predictions with MIMO Vision Transformers
Multi-input multi-output training improves network performance by optimizing multiple subnetworks simultaneously. In this paper, we …
Remy Sun, Clément Masson, Nicolas Thome, Matthieu Cord
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Reconciling feature sharing and multiple predictions with MIMO Vision Transformers
Swapping Semantic Contents for Mixing Images
Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data. In fact, the amount of …
Remy Sun, Clément Masson, Gilles Hénaff, Nicolas Thome, Matthieu Cord
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Swapping Semantic Contents for Mixing Images
Adapting Multi-input Multi-output Schemes to Vision Transformers
Multi-input multi-output models have proven capable of providing ensembling for free in convolutional neural networks by training …
Remy Sun, Clément Masson, Nicolas Thome, Matthieu Cord
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Adapting Multi-input Multi-output Schemes to Vision Transformers
Towards Efficient Feature Sharing in MIMO Architectures
Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork …
Remy Sun, Alexandre Rame, Clément Masson, Nicolas Thome, Matthieu Cord
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Towards Efficient Feature Sharing in MIMO Architectures
A theory of independent mechanisms for extrapolation in generative models
Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of …
Michel Besserve, Remy Sun, Dominik Janzing, Bernhard Schölkopf
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A theory of independent mechanisms for extrapolation in generative models
MixMo Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks
Recent strategies achieved ensembling “for free” by fitting concurrently diverse subnetworks inside a single base network. …
Alexandre Rame, Remy Sun, Matthieu Cord
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MixMo Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks
Semantic augmentation by mixing contents for semi-supervised learning
Leveraging unlabeled examples is a crucial issue for boosting performances in semi-supervised learning. In this work, we introduce the …
Remy Sun, Clément Masson, Gilles Hénaff, Nicolas Thome, Matthieu Cord
PDF Project
Counterfactuals uncover the modular structure of deep generative models
Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. …
Michel Besserve, Arash Mehrjou, Remy Sun, Bernhard Schölkopf
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Counterfactuals uncover the modular structure of deep generative models