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Visualizing the embedding space to explain the effect of knowledge distillation

2021-10-09 07:04:26
Hyun Seung Lee, Christian Wallraven

Abstract

Recent research has found that knowledge distillation can be effective in reducing the size of a network and in increasing generalization. A pre-trained, large teacher network, for example, was shown to be able to bootstrap a student model that eventually outperforms the teacher in a limited label environment. Despite these advances, it still is relatively unclear \emph{why} this method works, that is, what the resulting student model does 'better'. To address this issue, here, we utilize two non-linear, low-dimensional embedding methods (t-SNE and IVIS) to visualize representation spaces of different layers in a network. We perform a set of extensive experiments with different architecture parameters and distillation methods. The resulting visualizations and metrics clearly show that distillation guides the network to find a more compact representation space for higher accuracy already in earlier layers compared to its non-distilled version.

Abstract (translated)

URL

https://arxiv.org/abs/2110.04483

PDF

https://arxiv.org/pdf/2110.04483.pdf


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