LayerShuffle: Enhancing Robustness in Vision Transformers by Randomizing Layer Execution Order

Matthias Freiberger, Peter Kun, Anders Sundnes Løvlie, Sebastian Risi

Keywords: Vision Transformer; Distributed Vision Transformer; Decentralization; AI Robustness; Merged Models; Scalable AI; Edge AI; Random Execution Order

TL;DR: We introduce a set of techniques to enhance the robustness of vision transformers whose most important component is randomizing the execution order of attention modules at training time.

Abstract: Due to their architecture and how they are trained, artificial neural networks are typically not robust toward pruning, replacing, or shuffling layers at test time. However, such properties would be desirable for different applications, such as distributed neural network architectures where the order of execution cannot be guaranteed or parts of the network can fail during inference. In this work, we address these issues through a number of training approaches for vision transformers whose most important component is randomizing the execution order of attention modules at training time. With our proposed approaches, vision transformers are capable to adapt to arbitrary layer execution orders at test time assuming one tolerates a reduction (about 20\%) in accuracy at the same model size. We analyse the feature representations of our trained models as well as how each layer contributes to the models prediction based on its position during inference. Our analysis shows that layers learn to contribute differently based on their position in the network. Importantly, trained models can also be randomly merged with each other resulting in functional ("Frankenstein") models without loss of performance compared to the source models. Finally, we layer-prune our models at test time and find that their performance declines gracefully.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 384