Noise May Contain Transferable Knowledge: Understanding Semi-supervised
  Heterogeneous Domain Adaptation from an Empirical Perspective
Authors: name: Yuan Yao, name: Xiaopu Zhang, name: Yu Zhang, name: Jian Jin, name: Qiang Yang

Semi-supervised heterogeneous domain adaptation (SHDA) addresses learning
across domains with distinct feature representations and distributions, where
source samples are labeled while most target samples are unlabeled, with only a
small fraction labeled. Moreover, there is no one-to-one correspondence between
source and target samples. Although various SHDA methods have been developed to
tackle this problem, the nature of the knowledge transferred across
heterogeneous domains remains unclear. This paper delves into this question
from an empirical perspective. We conduct extensive experiments on about 330
SHDA tasks, employing two supervised learning methods and seven representative
SHDA methods. Surprisingly, our observations indicate that both the category
and feature information of source samples do not significantly impact the
performance of the target domain. Additionally, noise drawn from simple
distributions, when used as source samples, may contain transferable knowledge.
Based on this insight, we perform a series of experiments to uncover the
underlying principles of transferable knowledge in SHDA. Specifically, we
design a unified Knowledge Transfer Framework (KTF) for SHDA. Based on the KTF,
we find that the transferable knowledge in SHDA primarily stems from the
transferability and discriminability of the source domain. Consequently,
ensuring those properties in source samples, regardless of their origin (e.g.,
image, text, noise), can enhance the effectiveness of knowledge transfer in
SHDA tasks. The codes and datasets are available at
https://github.com/yyyaoyuan/SHDA.