  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 the parts,
represented by second-order pooling, onto unlabeled data in an adversarial
training setting. We demonstrate the effectiveness of the combined use by
conducting experiments on six state-of-the-art fine-grained datasets, which
include Aircrafts, Stanford Cars, CUB-200-2011, Oxford Flowers, Stanford Dogs,
and the recent Semi-Supervised iNaturalist-Aves. Experimental results clearly
show that our proposed method has better performance than the only previous
approach that examined this problem; it also obtained higher classification
accuracy with respect to the supervised learning methods with which we
compared.
