Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification

Cited by: 0|Bibtex|Views23|Links

Abstract:

Zero-shot learning strives to classify unseen categories for which no data is available during training. In the generalized variant, the test samples can further belong to seen or unseen categories. The state-of-the-art relies on Generative Adversarial Networks that synthesize unseen class features by leveraging class-specific semantic ...More

Code:

Data:

Your rating :
0

 

Tags
Comments