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Following Coteaching, generally in the literature, two models are used insample selection based approaches for training with noisy labels. Meanwhile, itis also well known that Dropout when present in a network trains an ensemble ofsub-networks. We show how to leverage this property of Dropout to train anexponential number of shared models, by training a single model with Dropout.We show how we can modify existing two model-based sample selectionmethodologies to use an exponential number of shared models. Not only is itmore convenient to use a single model with Dropout, but this approach alsocombines the natural benefits of Dropout with that of training an exponentialnumber of models, leading to improved results.

Dropout can Simulate Exponential Number of Models for Sample Selection Techniques