Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features

TL;DR

Pro$^2$ is a sample-efficient domain adaptation method that learns diverse orthogonal features and interpolates them to adapt pre-trained models to new distributions with limited target data.

Contribution

It introduces a lightweight approach that learns orthogonal predictive features and interpolates them, improving adaptation efficiency with small target datasets.

Findings

Pro$^2$ improves performance by 5-15% over prior methods.

Theoretically, it achieves better generalization through favorable bias-variance tradeoff.

Effective across multiple datasets and distribution shifts.

Abstract

Transfer learning with a small amount of target data is an effective and common approach to adapting a pre-trained model to distribution shifts. In some situations, target data labels may be expensive to obtain, so we may only have access to a limited number of target data points. To make the most of a very small target dataset, we propose a lightweight, sample-efficient approach that learns a diverse set of features and adapts to a target distribution by interpolating these features. Our approach, Project and Probe (Pro$^2$), first learns a linear projection that maps a pre-trained embedding onto orthogonal directions while being predictive of labels in the source dataset. The goal of this step is to learn a variety of predictive features, so that at least some of them remain useful after distribution shift. Pro$^2$ then learns a linear classifier on top of these projected features using a small target dataset. Theoretically, we find that Pro$^2$ results in more sample-efficient generalization by inducing a favorable bias-variance tradeoff. Our experiments on four datasets, with multiple distribution shift settings for each, show that Pro$^2$ improves performance by 5-15% when given limited target data compared to prior methods such as standard linear probing.