Noise May Contain Transferable Knowledge: Understanding Semi-supervised
  Heterogeneous Domain Adaptation from an Empirical Perspective
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.
67b70459ea22340afaaf416f
Yuan Yao
extracted_pending
2025-02-20T10:30:51.477Z
668bb3b14c25c09b01815a55
/avatars/5d46301dd5d7641e3da05b0ad560efee.svg
Yuan Yao
user
yyyaoyuan
67b70459ea22340afaaf4170
Xiaopu Zhang
admin_assigned
2025-02-20T17:11:57.564Z
67a2d9acd62b7924b4511564
/avatars/adc0c3d0e11acb64ba72c7914f5105db.svg
Xiaopu Zhang
user
xiaopz2
67b70459ea22340afaaf4171
Yu Zhang
67b70459ea22340afaaf4172
Jian Jin
67b70459ea22340afaaf4173
Qiang Yang
Noise May Contain Transferable Knowledge: Understanding Semi-supervised
  Heterogeneous Domain Adaptation from an Empirical Perspective
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.
Knowledge transfer in semi-supervised heterogeneous domain adaptation primarily relies on the transferability and discriminability of source domain samples, regardless of their feature representations or category.
semi-supervised heterogeneous domain adaptation SHDA knowledge transfer feature representations domain adaptation target domain source samples supervised learning noise feature information category information Knowledge Transfer Framework KTF