AdaFilter: Adaptive Filter Fine-Tuning for Deep Transfer Learning

Authors

  • Yunhui Guo University of California, San Diego
  • Yandong Li University of Central Florida
  • Liqiang Wang University of Central Florida
  • Tajana Rosing University of California, San Diego

DOI:

https://doi.org/10.1609/aaai.v34i04.5824

Abstract

There is an increasing number of pre-trained deep neural network models. However, it is still unclear how to effectively use these models for a new task. Transfer learning, which aims to transfer knowledge from source tasks to a target task, is an effective solution to this problem. Fine-tuning is a popular transfer learning technique for deep neural networks where a few rounds of training are applied to the parameters of a pre-trained model to adapt them to a new task. Despite its popularity, in this paper we show that fine-tuning suffers from several drawbacks. We propose an adaptive fine-tuning approach, called AdaFilter, which selects only a part of the convolutional filters in the pre-trained model to optimize on a per-example basis. We use a recurrent gated network to selectively fine-tune convolutional filters based on the activations of the previous layer. We experiment with 7 public image classification datasets and the results show that AdaFilter can reduce the average classification error of the standard fine-tuning by 2.54%.

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Published

2020-04-03

How to Cite

Guo, Y., Li, Y., Wang, L., & Rosing, T. (2020). AdaFilter: Adaptive Filter Fine-Tuning for Deep Transfer Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4060-4066. https://doi.org/10.1609/aaai.v34i04.5824

Issue

Section

AAAI Technical Track: Machine Learning