Deep Transfer Learning: Model Framework and Error Analysis

2024-10-12

arXiv.org

AI Summary

Small-sample downstream tasks (with $m$ samples) suffer from limited generalization performance. Method: We propose a multi-source deep transfer learning framework that leverages large-scale, multi-domain upstream data ($n gg m$) to enhance downstream generalization. Our approach integrates multi-domain feature disentanglement modeling with Lipschitz function theory, enabling rigorous theoretical analysis. Contribution/Results: We establish the first deep transfer learning theory with provable error bounds, supporting automatic identification of shared versus domain-specific features and yielding interpretable mappings of upstream–downstream feature contributions. Theoretically, partial or full transfer accelerates the downstream convergence rate from $O(m^{-1/(2(d+2))})$ to the optimal $O( ilde{m}^{-1/2} + n^{-1/(2(d+2))})$. Empirical validation on image classification and regression tasks confirms both performance gains and theoretical consistency.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Learning & Optimization for CV
• Multiagent Systems: Multiagent Learning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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 $ ilde{O}(m^{-frac{1}{2(d+2)}}+n^{-frac{1}{2(d+2)}})$ ("no transfer") to $ ilde{O}(m^{-frac{1}{2(d^*+3)}} + n^{-frac{1}{2(d+2)}})$ ("partial transfer"), and even to $ ilde{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.

Problem

• Leveraging multi-domain upstream data to enhance downstream task performance
• Identifying shared and specific features for precise information transfer
• Improving convergence rates in downstream supervised learning tasks

Innovation

• Deep transfer learning framework for multi-domain data
• Automatic identification of shared and specific features
• Improved convergence rate for downstream tasks