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Virtual presentation / poster accept

RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data

Sangwoo Mo · Jong-Chyi Su · Chih-Yao Ma · Mahmoud Assran · Ishan Misra · Licheng Yu · Sean Bell

Keywords: [ uncurated data ] [ representation learning ] [ semi-supervised learning ] [ Deep Learning and representational learning ]


Abstract:

Semi-supervised learning aims to train a model using limited labels. State-of-the-art semi-supervised methods for image classification such as PAWS rely on self-supervised 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 that models densities using kernel density estimation. From this probabilistic perspective, we calibrate its prediction based on the densities of labeled and unlabeled data, which leads to a simple closed-form solution from the Bayes' rule. We demonstrate that RoPAWS significantly improves PAWS for uncurated Semi-iNat by +5.3% and curated ImageNet by +0.4%.

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