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Layer-wise Pruning and Auto-tuning of Layer-wise Learning Rates in Fine-tuning of Deep Networks

2020-02-14 14:24:40
Youngmin Ro, Jin Young Choi
arXiv_CV
       
Abstract

Existing fine-tuning methods use a single learning rate over all layers. In this paper, first, we discuss that trends of layer-wise weight variations by fine-tuning using a single learning rate do not match the well-known notion that lower-level layers extract general features and higher-level layers extract specific features. Based on our discussion, we propose an algorithm that improves fine-tuning performance and reduces network complexity through layer-wise pruning and auto-tuning of layer-wise learning rates. Through in-depth experiments on image retrieval (CUB-200-2011, Stanford online products, and Inshop) and fine-grained classification (Stanford cars, Aircraft) datasets, the effectiveness of the proposed algorithm is verified.

Abstract (translated)

URL

https://arxiv.org/abs/2002.06048

PDF

https://arxiv.org/pdf/2002.06048.pdf

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