Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders

Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space. As labeled images are expensive, one direction is to augment the dataset by generating either images or image features. However, the former misses fine-grained...

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Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 8239 - 8247
Hauptverfasser:	Schonfeld, Edgar, Ebrahimi, Sayna, Sinha, Samarth, Darrell, Trevor, Akata, Zeynep
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2019
Schlagworte:	Autoencoders Benchmark testing Computational modeling Computer vision Deep Learning Few shot learning Pattern recognition Representation Learning Semantics Visualization Zero shot learning
ISSN:	1063-6919
Online-Zugang:	Volltext
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