Joint Learning of Generative Translator and Classifier for Visually Similar Classes

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In this paper, we propose a Generative Translation Classification Network (GTCN) for improving visual classification accuracy in settings where classes are visually similar and data is scarce. For this purpose, we propose joint learning from a scratch to train a classifier and a generative stochastic translation network end-to-end. The translation network is used to perform on-line data augmentation across classes, whereas previous works have mostly involved domain adaptation. To help the model further benefit from this data-augmentation, we introduce an adaptive fade-in loss and a quadruplet loss. We perform experiments on multiple datasets to demonstrate the proposed method's performance in varied settings. Of particular interest, training on 40% of the dataset is enough for our model to surpass the performance of baselines trained on the full dataset. When our architecture is trained on the full dataset, we achieve comparable performance with state-of-the-art methods despite using a light-weight architecture.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.8, pp.219160 - 219173

ISSN
2169-3536
DOI
10.1109/ACCESS.2020.3042302
URI
http://hdl.handle.net/10203/279458
Appears in Collection
EE-Journal Papers(저널논문)
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