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This paper tackles the problem of semi-supervised learning when the set oflabeled samples is limited to a small number of images per class, typicallyless than 10, problem that we refer to as barely-supervised learning. Weanalyze in depth the behavior of a state-of-the-art semi-supervised method,FixMatch, which relies on a weakly-augmented version of an image to obtainsupervision signal for a more strongly-augmented version. We show that itfrequently fails in barely-supervised scenarios, due to a lack of trainingsignal when no pseudo-label can be predicted with high confidence. We propose amethod to leverage self-supervised methods that provides training signal in theabsence of confident pseudo-labels. We then propose two methods to refine thepseudo-label selection process which lead to further improvements. The firstone relies on a per-sample history of the model predictions, akin to a votingscheme. The second iteratively updates class-dependent confidence thresholds tobetter explore classes that are under-represented in the pseudo-labels. Ourexperiments show that our approach performs significantly better on STL-10 inthe barely-supervised regime, e.g. with 4 or 8 labeled images per class.

Barely-Supervised Learning: Semi-Supervised Learning with very few labeled images