Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical Perspective
Yuan Yao, Xiaopu Zhang, Yu Zhang, Jian Jin, Qiang Yang
2025-02-20
Summary
This paper talks about a surprising discovery in how AI learns from different types of data. The researchers found that even random noise can help AI models learn useful information, which challenges our understanding of how these systems work.
What's the problem?
When teaching AI to work with different types of data (like text and images), we usually think the AI needs to learn from real, labeled examples. But it's not always clear what exactly the AI is learning or transferring between these different types of data. This is especially tricky when we don't have many labeled examples to work with.
What's the solution?
The researchers did a ton of experiments, testing different AI learning methods on over 300 tasks. They found something unexpected: the AI could learn just as well from random noise as it could from real data. They then created a new framework to understand why this works, focusing on how well information can be transferred and distinguished between different types of data.
Why it matters?
This matters because it could change how we train AI systems, especially when we don't have a lot of labeled data to work with. If random noise can be just as helpful as real data in some cases, it might make it easier and cheaper to train AI for new tasks. It also challenges what we thought we knew about how AI learns, which could lead to new and more efficient ways of developing AI systems that can work with many different types of information.
Abstract
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 <PRE_TAG>knowledge transfer</POST_TAG>red 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 <PRE_TAG>knowledge transfer</POST_TAG> in SHDA tasks. The codes and datasets are available at https://github.com/yyyaoyuan/SHDA.