PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information

Abstract

Few-shot classification (FSC) requires training models using a few (typically one to five) data points per class. Meta-learning has proven to be able to learn a parametrized model for FSC by training on various other classification tasks. In this work, we propose PLATINUM (semi-suPervised modeL Agnostic meTa learnIng usiNg sUbmodular Mutual information ), a novel semi-supervised model agnostic meta learning framework that uses the submodular mutual information (SMI) functions to boost the performance of FSC. PLATINUM leverages unlabeled data in the inner and outer loop using SMI functions during meta-training and obtains richer metalearned parameterizations. We study the performance of PLATINUM in two scenarios -1) where the unlabeled data points belong to the same set of classes as the labeled set of a certain episode, and 2) where there exist out-ofdistribution classes that do not belong to the labeled set. We evaluate our method on various settings on the miniImageNet, tieredImageNet and CIFAR-FS datasets. Our experiments show that PLATINUM outperforms MAML and semisupervised approaches like pseduo-labeling for semi-supervised FSC, especially for small ratio of labeled to unlabeled samples.