Adapting Multi-input Multi-output Schemes to Vision Transformers

Remy Sun, Clément Masson, Nicolas Thome, Matthieu Cord
June 2022
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Abstract

Multi-input multi-output models have proven capable of providing ensembling for free in convolutional neural networks by training independent subnetworks that can be ensembled. How these architectures translate to vision transformer architectures is however unclear. In this paper, we introduce MixViT, the first multi-input multi-output framework for vision transformers. After discussing MixViT and the novel source embedding mechanism it relies on for subnetwork separation, we show how much it improves on standard transformers in a preliminary study on CIFAR100.

Type
Conference paper
Publication
In Transformers and attention for Vision workshop at CVPR

The work in this paper was extended in a preprint: “Reconciling feature sharing and multiple predictions with MIMO Vision Transformers”

Mixing Sample Data Augmentation Ensembling Subnetworks Vision Transformers Content Manipulation
Remy Sun
Remy Sun
Postdoctoral researcher

I am a postdoctoral researcher at Inria Sophia Antipolis (MAASAI) team working on interactions between autonomous driving systems and maps (as knowledge bases).