Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning

TL;DR

This paper explores using zero-shot pseudolabeling to improve CLIP's prompt tuning for image classification, achieving significant accuracy gains across various learning paradigms while reducing class bias.

Contribution

It introduces a unified framework for pseudolabeling with CLIP, leveraging zero-shot capabilities to enhance prompt tuning across multiple learning paradigms.

Findings

Prompt tuning with pseudolabels improves CLIP accuracy significantly.

Iterative pseudolabel refinement enhances performance across paradigms.

Prompt tuning reduces class bias compared to traditional pseudolabeling.

Abstract

Fine-tuning vision-language models (VLMs) like CLIP to downstream tasks is often necessary to optimize their performance. However, a major obstacle is the limited availability of labeled data. We study the use of pseudolabels, i.e., heuristic labels for unlabeled data, to enhance CLIP via prompt tuning. Conventional pseudolabeling trains a model on labeled data and then generates labels for unlabeled data. VLMs' zero-shot capabilities enable a "second generation" of pseudolabeling approaches that do not require task-specific training on labeled data. By using zero-shot pseudolabels as a source of supervision, we observe that learning paradigms such as semi-supervised, transductive zero-shot, and unsupervised learning can all be seen as optimizing the same loss function. This unified view enables the development of versatile training strategies that are applicable across learning paradigms. We investigate them on image classification tasks where CLIP exhibits limitations, by varying prompt modalities, e.g., textual or visual prompts, and learning paradigms. We find that (1) unexplored prompt tuning strategies that iteratively refine pseudolabels consistently improve CLIP accuracy, by 19.5 points in semi-supervised learning, by 28.4 points in transductive zero-shot learning, and by 15.2 points in unsupervised learning, and (2) unlike conventional semi-supervised pseudolabeling, which exacerbates model biases toward classes with higher-quality pseudolabels, prompt tuning leads to a more equitable distribution of per-class accuracy. The code to reproduce the experiments is at https://github.com/BatsResearch/menghini-neurips23-code.