Dense Adversarial Transfer Learning Based On Class-Invariance
Bach-Tung Pham, Ting-Yu Wang, Phuong Le Thi, Khai-Thinh Nguyen, Yuan-Shan Lee, Tzu-Chiang Tai, Jia-Ching Wang
Abstract
This work proposes the dense adversarial transfer learning based on class-invariance, which is a novel, unsupervised, conditional adversarial domain adaptation approach. The proposed framework concatenates feature maps from the last layer of each backbone’s block to improve transfer learning; these features are weighted and densely connected to the features of each block along with the gradient-reversal layer. Classifiers are also added to the domain discriminators so that the network not only retains the classifying abilities when learning the domain-invariant features, but also has its domain adaptation abilities improved. In the experiment, the benchmark dataset Office-31 is used to compare the performance of similar existing frameworks. In three transfer tasks, the proposed method enhances the accuracy by approximately 3% to 5%, demonstrating the improvement provided by the proposed network towards unsupervised domain adaptation.
BibTeX
@inproceedings{icassp2023_denseadversarial,
title = {Dense Adversarial Transfer Learning Based On Class-Invariance},
author = {Bach-Tung Pham and Ting-Yu Wang and Phuong Le Thi and Khai-Thinh Nguyen and Yuan-Shan Lee and Tzu-Chiang Tai and Jia-Ching Wang},
booktitle = {ICASSP 2023},
year = {2023}
}