Geometry Aware Deep Metric Learning
A diverse range of applications in computer vision benefit from the data representations which are dense and compact, yet discriminative enough to learn the subtle changes in the data. Such representation learning seems necessary especially in the Zero Shot Learning applications where the train and the test classes are mutually exclusive. In other words, the learned representations should be discriminative enough to identify the minute cues in the data samples such that the unseen data can be...[Show more]
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