Deep Transfer Learning: Model Framework and Error Analysis
This addresses the challenge of data scarcity in downstream tasks for machine learning practitioners, offering a method to enhance performance through transfer learning, though it appears incremental as it builds on existing transfer learning concepts with theoretical refinements.
This paper tackles the problem of transferring knowledge from multi-domain upstream data to a single-domain downstream task with limited samples, presenting a framework that identifies shared and domain-specific features for improved performance. The result shows significant theoretical improvements in convergence rates, reducing from O(m^{-1/(2(d+2))}) to as low as O(m^{-1/2}) in ideal cases, with empirical validation on image classification and regression datasets.
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 offers several intriguing features. First, it allows the existence of both shared and domain-specific features across multi-domain data and provides a framework for automatic identification, achieving precise transfer and utilization of information. Second, the framework explicitly identifies upstream features that contribute to downstream tasks, establishing clear relationships between upstream domains and downstream tasks, thereby enhancing interpretability. Error analysis shows that 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 supported by empirical experiments on image classification and regression datasets.