Transfer Learning in a Transductive Setting Transfer Learning in a Transductive Setting
Paper summary This paper describes how to attack the zero-, one-, or few-shot recognition problem, where we have a fair amount of training data for some classes, but none or very few for some other classes. It does this using three different techniques, all combined in a single framework: using semantically-meaningful mid-layer knowledge (attributes), building a graph on new classes to exploit the manifold structure, and finally by using an attribute-based representation for building the graph structure (rather than low-level features), which improves performance. The method is evaluated on 3 different datasets (Animals with Attributes, ImageNet, and MPII Cooking composites), and shows improved performance on all compared to the state-of-the-art (slightly).

Summary by NIPS Conference Reviews 5 years ago
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