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Title: PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information
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 in- formation (SMI) functions to boost the perfor- mance of FSC. PLATINUM leverages unlabeled data in the inner and outer loop using SMI func- tions during meta-training and obtains richer meta- learned parameterizations. We study the per- formance of PLATINUM in two scenarios - 1) where the unlabeled data points belong to the same set of classes as the labeled set of a cer- tain episode, and 2) where there exist out-of- distribution classes that do not belong to the la- beled set. We evaluate our method on various settings on the miniImageNet, tieredImageNet and CIFAR-FS datasets. Our experiments show that PLATINUM outperforms MAML and semi- supervised approaches like pseduo-labeling for semi-supervised FSC, especially for small ratio of labeled to unlabeled samples.  more » « less
Award ID(s):
2107449
PAR ID:
10350224
Author(s) / Creator(s):
; ; ;
Editor(s):
Chaudhuri, Kamalika; Jegelka, Stefanie; Song, Le; Szepesyari, Csaba; Niu, Gang; Sabato, Sivan
Date Published:
Journal Name:
International Conference on Machine Learning
Volume:
162
Page Range / eLocation ID:
12826--12842
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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