Unsupervised Finetuning

TL;DR

This paper introduces unsupervised finetuning, a method to adapt pretrained models to small unlabeled target datasets by combining source data and target data, improving transfer performance.

Contribution

It proposes two novel strategies, sparse source data replaying and data mixing, to enhance unsupervised finetuning effectiveness.

Findings

Improved transfer performance over naive methods.

Source data is crucial for effective unsupervised finetuning.

Strategies are effective across multiple datasets.

Abstract

This paper studies "unsupervised finetuning", the symmetrical problem of the well-known "supervised finetuning". Given a pretrained model and small-scale unlabeled target data, unsupervised finetuning is to adapt the representation pretrained from the source domain to the target domain so that better transfer performance can be obtained. This problem is more challenging than the supervised counterpart, as the low data density in the small-scale target data is not friendly for unsupervised learning, leading to the damage of the pretrained representation and poor representation in the target domain. In this paper, we find the source data is crucial when shifting the finetuning paradigm from supervise to unsupervise, and propose two simple and effective strategies to combine source and target data into unsupervised finetuning: "sparse source data replaying", and "data mixing". The motivation of the former strategy is to add a small portion of source data back to occupy their pretrained representation space and help push the target data to reside in a smaller compact space; and the motivation of the latter strategy is to increase the data density and help learn more compact representation. To demonstrate the effectiveness of our proposed ``unsupervised finetuning'' strategy, we conduct extensive experiments on multiple different target datasets, which show better transfer performance than the naive strategy.