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Found 2 papers, 1 papers with code
Fine-Grained Adversarial Semi-supervised Learning
Our approach leverages unlabeled data with an adversarial optimization strategy in which the internal features representation is obtained with a second-order pooling model.
Class-incremental Learning with Pre-allocated Fixed Classifiers
Contrarily to the standard expanding classifier, this allows: (a) the output nodes of future unseen classes to firstly see negative samples since the beginning of learning together with the positive samples that incrementally arrive; (b) to learn features that do not change their geometric configuration as novel classes are incorporated in the learning model.
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