Teaching Squeeze-and-Excitation PyramidNet for Imbalanced Image Classification with GAN-based Curriculum Learning
Jing Liu, Angang Du, Chao Wang, Haiyong Zheng, Nan Wang, Bing Zheng · 2018
Image classification with datasets that suffer from great imbalanced class distribution is a challenging task in computer vision field. In many real-world problems, the datasets are typically imbalanced and have a serious impact on the performance of classifiers. Although deep convolutional neural networks (DCNNs) have shown remarkable performance on image classification tasks in recent years, there are still few effective deep learning algorithms specifically for imbalanced image classification problems. To solve imbalanced image classification problem, in this paper, we explore a new deep learning algorithm called Squeeze-and-Excitation Deep Pyramidal Residual Network (SE-PyramidNet) combining with Generative Adversarial Network (GAN)-based curriculum learning. Firstly, we construct the refined Deep Pyramidal Residual Network by embedding the “Squeeze-and-Excitation” (SE) blocks. Secondly, towards the class imbalance problem, we adopt GAN to generate samples of minority classes. Finally, we draw lessons from the curriculum learning strategy by teaching our classifier training from original easy samples to generated complex samples, which improves the classification ability. Experimental results show that our method achieves around 0.5% gains for accuracy and 0.02 gains for F1 score respectively outperforming the state-of-the-art DCNNs.