End-to-end Learning, with or without Labels
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We present an approach for end-to-end learning that allows one to jointly learn a feature representation from unlabeled data (with or without labeled data) and predict labels for unlabeled data. The feature representation is assumed to be specified in a differentiable programming framework, that is, as a parameterized mapping amenable to automatic differentiation. The proposed approach can be used with any amount of labeled and unlabeled data, gracefully adjusting to the amount of supervision. We provide experimental results illustrating the effectiveness of the approach.
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