Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity. While prior work has addressed intra-class variation using localization and segmentation techniques, inter-class similarity may also affect feature learning and reduce classification performance. In this work, we address this problem using a novel optimization procedure for the end-to-end neural network training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces overfitting by intentionally {introducing confusion} in the activations. With PC regularization, we obtain state-of-the-art performance on six of the most widely-used FGVC datasets and demonstrate improved localization ability. {PC} is easy to implement, does not need excessive hyperparameter tuning during training, and does not add significant overhead during test time.
@article{arxiv.1705.08016,
title = {Pairwise Confusion for Fine-Grained Visual Classification},
author = {Abhimanyu Dubey and Otkrist Gupta and Pei Guo and Ramesh Raskar and Ryan Farrell and Nikhil Naik},
journal= {arXiv preprint arXiv:1705.08016},
year = {2018}
}