Task-Adaptive Clustering for Semi-Supervised Few-Shot Classification

TL;DR

This paper introduces a semi-supervised few-shot learning method that uses task-conditioned clustering in a new projection space, achieving state-of-the-art results on miniImageNet and tieredImageNet datasets.

Contribution

The paper proposes a novel task-adaptive clustering approach with explicit task-conditioning for semi-supervised few-shot classification, improving performance with unlabeled data.

Findings

Achieves state-of-the-art semi-supervised few-shot classification accuracy.

Demonstrates robustness to distractor samples outside candidate classes.

Shows effective control of task-conditioning based on clustering updates.

Abstract

Few-shot learning aims to handle previously unseen tasks using only a small amount of new training data. In preparing (or meta-training) a few-shot learner, however, massive labeled data are necessary. In the real world, unfortunately, labeled data are expensive and/or scarce. In this work, we propose a few-shot learner that can work well under the semi-supervised setting where a large portion of training data is unlabeled. Our method employs explicit task-conditioning in which unlabeled sample clustering for the current task takes place in a new projection space different from the embedding feature space. The conditioned clustering space is linearly constructed so as to quickly close the gap between the class centroids for the current task and the independent per-class reference vectors meta-trained across tasks. In a more general setting, our method introduces a concept of controlling the degree of task-conditioning for meta-learning: the amount of task-conditioning varies with the number of repetitive updates for the clustering space. Extensive simulation results based on the miniImageNet and tieredImageNet datasets show state-of-the-art semi-supervised few-shot classification performance of the proposed method. Simulation results also indicate that the proposed task-adaptive clustering shows graceful degradation with a growing number of distractor samples, i.e., unlabeled sample images coming from outside the candidate classes.