Abstract:We present a novel framework for the problem of transfer learning between few-shot source and target domains, using synthetic attributes in addition to convolutional neural networks that are pre-trained on larger image corpora. In these corpora, no labeled instances of the target domains are present, though they may contain instances of their superclasses. Using probabilistic inference over predicted classes and inferred attributes, we developed a metalearning ensemble method that builds upon that of [10]. Thi… Show more
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