2023
DOI: 10.48550/arxiv.2302.14483
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RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data

Abstract: Semi-supervised learning aims to train a model using limited labels. State-of-theart semi-supervised methods for image classification such as PAWS rely on selfsupervised representations learned with large-scale unlabeled but curated data. However, PAWS is often less effective when using real-world unlabeled data that is uncurated, e.g., contains out-of-class data. We propose RoPAWS, a robust extension of PAWS that can work with real-world unlabeled data. We first reinterpret PAWS as a generative classifier tha… Show more

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