Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder

State of the art (SOTA) few-shot learning (FSL) methods suffer significant performance drop in the presence of domain differences between source and target datasets. The strong discrimination ability on the source dataset does not necessarily translate to high classification accuracy on the target d...

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Vydané v:	Proceedings / IEEE International Conference on Computer Vision s. 9404 - 9414
Hlavní autori:	Liang, Hanwen, Zhang, Qiong, Dai, Peng, Lu, Juwei
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.10.2021
Predmet:	Boosting Computer vision Correlation Feature extraction Optimization and learning methods Predictive models Recognition and classification Representation learning Transfer learning Transfer/Low-shot/Semi/Unsupervised Learning
ISSN:	2380-7504
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