Generating Classification Weights With GNN Denoising Autoencoders for Few-Shot Learning

Given an initial recognition model already trained on a set of base classes, the goal of this work is to develop a meta-model for few-shot learning. The meta-model, given as input some novel classes with few training examples per class, must properly adapt the existing recognition model into a new m...

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Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 21 - 30
Hauptverfasser:	Gidaris, Spyros, Komodakis, Nikos
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2019
Schlagworte:	Adaptation models Autoencoders Categorization Computer architecture Deep Learning Few shot learning Graph neural networks Image reconstruction Noise reduction Recognition: Detection Retrieval Technological innovation Training Vectors
ISSN:	1063-6919
Online-Zugang:	Volltext
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