CUAL: Continual Uncertainty-aware Active Learner

TL;DR

CUAL is a continual learning framework that actively and uncertainty-awarely selects samples for labeling, enabling AI systems to adapt to new and unknown classes with limited labeling budgets.

Contribution

It introduces a novel uncertainty-aware active learning approach for continual adaptation to unseen classes in a realistic setting.

Findings

Effective detection of novel classes through uncertainty estimation

Improved continual learning performance across multiple datasets

Compatibility with various backbone models like ViT

Abstract

AI deployed in many real-world use cases should be capable of adapting to novelties encountered after deployment. Here, we consider a challenging, under-explored and realistic continual adaptation problem: a deployed AI agent is continuously provided with unlabeled data that may contain not only unseen samples of known classes but also samples from novel (unknown) classes. In such a challenging setting, it has only a tiny labeling budget to query the most informative samples to help it continuously learn. We present a comprehensive solution to this complex problem with our model "CUAL" (Continual Uncertainty-aware Active Learner). CUAL leverages an uncertainty estimation algorithm to prioritize active labeling of ambiguous (uncertain) predicted novel class samples while also simultaneously pseudo-labeling the most certain predictions of each class. Evaluations across multiple datasets, ablations, settings and backbones (e.g. ViT foundation model) demonstrate our method's effectiveness. We will release our code upon acceptance.