Deep Transfer Learning: Model Framework and Error Analysis

• URL: http://arxiv.org/abs/2410.09383v1
• Date: Sat, 12 Oct 2024 06:24:35 GMT
• Title: Deep Transfer Learning: Model Framework and Error Analysis
• Authors: Yuling Jiao, Huazhen Lin, Yuchen Luo, Jerry Zhijian Yang,
• Abstract summary: This paper presents a framework for deep transfer learning with a large number of samples $n$ to a single-domain downstream task. We show that the transfer under our framework can significantly improve the convergence rate for learning Lipschitz functions in downstream supervised tasks.
• Score: 4.898032902660655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a framework for deep transfer learning, which aims to leverage information from multi-domain upstream data with a large number of samples $n$ to a single-domain downstream task with a considerably smaller number of samples $m$, where $m \ll n$, in order to enhance performance on downstream task. Our framework has several intriguing features. First, it allows the existence of both shared and specific features among multi-domain data and provides a framework for automatic identification, achieving precise transfer and utilization of information. Second, our model framework explicitly indicates the upstream features that contribute to downstream tasks, establishing a relationship between upstream domains and downstream tasks, thereby enhancing interpretability. Error analysis demonstrates that the transfer under our framework can significantly improve the convergence rate for learning Lipschitz functions in downstream supervised tasks, reducing it from $\tilde{O}(m^{-\frac{1}{2(d+2)}}+n^{-\frac{1}{2(d+2)}})$ ("no transfer") to $\tilde{O}(m^{-\frac{1}{2(d^*+3)}} + n^{-\frac{1}{2(d+2)}})$ ("partial transfer"), and even to $\tilde{O}(m^{-1/2}+n^{-\frac{1}{2(d+2)}})$ ("complete transfer"), where $d^* \ll d$ and $d$ is the dimension of the observed data. Our theoretical findings are substantiated by empirical experiments conducted on image classification datasets, along with a regression dataset.

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