# Pairwise Confusion for Fine-Grained Visual Classification

## TL;DR

This paper introduces Pairwise Confusion, a novel regularization method for fine-grained visual classification that reduces overfitting by intentionally introducing confusion in neural network activations, leading to state-of-the-art results.

## Contribution

The paper proposes Pairwise Confusion, a simple yet effective regularization technique that improves fine-grained classification performance without complex hyperparameter tuning.

## Key findings

- Achieves state-of-the-art results on six FGVC datasets.
- Improves localization ability of models.
- Does not significantly increase training or testing overhead.

## Abstract

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.