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 meta-learned parameterizations for meta-test. 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-of-distribution classes that do not belong to the labeled set. We evaluate our method on various settings on the miniImageNet, tieredImageNet and Fewshot-CIFAR100 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 examples per class.
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Open-World Semi-Supervised Learning
A fundamental limitation of applying semi-supervised learning in real-world settings is the assumption that unlabeled test data contains only classes previously
encountered in the labeled training data. However, this assumption rarely holds for
data in-the-wild, where instances belonging to novel classes may appear at testing
time. Here, we introduce a novel open-world semi-supervised learning setting
that formalizes the notion that novel classes may appear in the unlabeled test data.
In this novel setting, the goal is to solve the class distribution mismatch between
labeled and unlabeled data, where at the test time every input instance either needs
to be classified into one of the existing classes or a new unseen class needs to be
initialized. To tackle this challenging problem, we propose ORCA, an end-to-end
deep learning approach that introduces uncertainty adaptive margin mechanism to
circumvent the bias towards seen classes caused by learning discriminative features
for seen classes faster than for the novel classes. In this way, ORCA reduces the gap
between intra-class variance of seen with respect to novel classes. Experiments on
image classification datasets and a single-cell annotation dataset demonstrate that
ORCA consistently outperforms alternative baselines, achieving 25% improvement
on seen and 96% improvement on novel classes of the ImageNet dataset.
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- Award ID(s):
- 1835598
- NSF-PAR ID:
- 10396196
- Date Published:
- Journal Name:
- International Conference on Learning Representations (ICLR)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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