Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation

类别压倒性：互条件混合目标域自适应

Pengcheng Xu,1 Boyu Wang, 1, 2 * Charles Ling

彭程旭1，王博宇1, 2*，Charles Ling

1 Western University, London, ON N6A 5B7, Canada 2 Vector Institute, Toronto, ON M5G 1M1, Canada pxu67@uwo.ca, bwang@csd.uwo.ca, charles.ling@uwo.ca

1 加拿大西安大略大学，伦敦市，N6A 5B7 2 加拿大向量研究所，多伦多市，M5G 1M1 pxu67@uwo.ca, bwang@csd.uwo.ca, charles.ling@uwo.ca

Abstract

摘要

Current methods of blended targets domain adaptation (BTDA) usually infer or consider domain label information but under emphasize hybrid categorical feature struc- tures of targets, which yields limited performance, especially under the label distribution shift. We demonstrate that domain labels are not directly necessary for BTDA if categorical distributions of various domains are sufficiently aligned even facing the imbalance of domains and the label distribution shift of classes. However, we observe that the cluster assumption in BTDA does not comprehensi