Scalable Greedy Algorithms for Transfer Learning

TL;DR

This paper introduces scalable greedy algorithms for transfer learning that select and combine source hypotheses efficiently, achieving state-of-the-art results in computer vision tasks with limited data.

Contribution

It proposes a novel algorithm for source hypothesis and feature selection in transfer learning, with theoretical guarantees and improved computational efficiency.

Findings

Achieves state-of-the-art results on three datasets

Outperforms transfer learning and feature selection baselines

Effective in small-sample scenarios

Abstract

In this paper we consider the binary transfer learning problem, focusing on how to select and combine sources from a large pool to yield a good performance on a target task. Constraining our scenario to real world, we do not assume the direct access to the source data, but rather we employ the source hypotheses trained from them. We propose an efficient algorithm that selects relevant source hypotheses and feature dimensions simultaneously, building on the literature on the best subset selection problem. Our algorithm achieves state-of-the-art results on three computer vision datasets, substantially outperforming both transfer learning and popular feature selection baselines in a small-sample setting. We also present a randomized variant that achieves the same results with the computational cost independent from the number of source hypotheses and feature dimensions. Also, we theoretically prove that, under reasonable assumptions on the source hypotheses, our algorithm can learn effectively from few examples.