2021
DOI: 10.48550/arxiv.2110.05848
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Fine-Grained Adversarial Semi-supervised Learning

Abstract: In this paper we exploit Semi-Supervised Learning (SSL) to increase the amount of training data to improve the performance of Fine-Grained Visual Categorization (FGVC). This problem has not been investigated in the past in spite of prohibitive annotation costs that FGVC requires. Our approach leverages unlabeled data with an adversarial optimization strategy in which the internal features representation is obtained with a second-order pooling model. This combination allows to back-propagate the information of … Show more

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References 62 publications
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