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Few-shot learning aims to handle previously unseen tasks using only a smallamount of new training data. In preparing (or meta-training) a few-shotlearner, however, massive labeled data are necessary. In the real world,unfortunately, labeled data are expensive and/or scarce. In this work, wepropose a few-shot learner that can work well under the semi-supervised settingwhere a large portion of training data is unlabeled. Our method employsexplicit task-conditioning in which unlabeled sample clustering for the currenttask takes place in a new projection space different from the embedding featurespace. The conditioned clustering space is linearly constructed so as toquickly close the gap between the class centroids for the current task and theindependent per-class reference vectors meta-trained across tasks. In a moregeneral setting, our method introduces a concept of controlling the degree oftask-conditioning for meta-learning: the amount of task-conditioning varieswith the number of repetitive updates for the clustering space. Extensivesimulation results based on the miniImageNet and tieredImageNet datasets showstate-of-the-art semi-supervised few-shot classification performance of theproposed method. Simulation results also indicate that the proposedtask-adaptive clustering shows graceful degradation with a growing number ofdistractor samples, i.e., unlabeled sample images coming from outside thecandidate classes.

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