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Exploring the Road Graph in Trajectory Forecasting for Autonomous Driving
As Deep Learning tackles complex tasks like trajectory forecasting in autonomous vehicles, a number of new challenges emerge. In …
Remy Sun, Diane Lingrand, Frederic Precioso
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Exploring the Road Graph in Trajectory Forecasting for Autonomous Driving
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
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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
Exploiting the modularity of deep networks to generate visual counterfactuals
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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KS(conf) A Light-Weight Test if a ConvNet Operates Outside of Its Specifications
Computer vision systems for automatic image categorization have become accurate and reliable enough that they can run continuously for …
Remy Sun, Christoph Lampert
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