36 citations · 60 across the 5 of their papers we have counts for
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cs.CV2020★ 4 cited
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning
John Cai, Bill Cai, Sheng Mei Shen
While many deep learning methods have seen significant success in tackling the problem of domain adaptation and few-shot learning separately, far fewer methods are able to jointly…
cs.CV2020★ 19 cited
Cross-Domain Few-Shot Learning with Meta Fine-Tuning
John Cai, Sheng Mei Shen
In this paper, we tackle the new Cross-Domain Few-Shot Learning benchmark proposed by the CVPR 2020 Challenge. To this end, we build upon state-of-the-art methods in domain adaptat…
cs.CV2019★ 36 cited
Anomaly Detection with Adversarial Dual Autoencoders
Ha Son Vu, Daisuke Ueta, Kiyoshi Hashimoto +3
Semi-supervised and unsupervised Generative Adversarial Networks (GAN)-based methods have been gaining popularity in anomaly detection task recently. However, GAN training is somew…