Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in
  Fine-tuning of Deep Networks   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. The proposed
algorithm has verified the effectiveness by achieving state-of-the-art
performance on the image retrieval benchmark datasets (CUB-200, Cars-196,
Stanford online product, and Inshop). Code is available at
https://github.com/youngminPIL/AutoLR.

11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn