title: Noise May Contain Transferable Knowledge: Understanding Semi-supervised
    Heterogeneous Domain Adaptation from an Empirical Perspective
summary: 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.
paper_authors._id: 67b70459ea22340afaaf416f
paper_authors.name: Yuan Yao
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-02-20T10:30:51.477Z
paper_authors._id: 668bb3b14c25c09b01815a55
paper_authors.avatarUrl: /avatars/5d46301dd5d7641e3da05b0ad560efee.svg
paper_authors.fullname: Yuan Yao
paper_authors.type: user
paper_authors.user: yyyaoyuan
paper_authors._id: 67b70459ea22340afaaf4170
paper_authors.name: Xiaopu Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:11:57.564Z
paper_authors._id: 67a2d9acd62b7924b4511564
paper_authors.avatarUrl: /avatars/adc0c3d0e11acb64ba72c7914f5105db.svg
paper_authors.fullname: Xiaopu Zhang
paper_authors.type: user
paper_authors.user: xiaopz2
paper_authors._id: 67b70459ea22340afaaf4171
paper_authors.name: Yu Zhang
paper_authors._id: 67b70459ea22340afaaf4172
paper_authors.name: Jian Jin
paper_authors._id: 67b70459ea22340afaaf4173
paper_authors.name: Qiang Yang
paper_title: Noise May Contain Transferable Knowledge: Understanding Semi-supervised
    Heterogeneous Domain Adaptation from an Empirical Perspective
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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