Handling class imbalance of radio frequency interference in deep learning-based fast radio burst search pipelines using a deep convolutional generative adversarial network

This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning. This issue is caused by the class imbalance of the radio frequency interference samples in the training dataset, and one solution is applied to improve the distribution of the training data by augmenting minority class samples using a deep convolutional generative adversarial network. Experimental results demonstrate that retraining the deep learning model with the newly generated dataset leads to a new fast radio burst classifier, which effectively reduces false positives caused by periodic wide-band impulsive radio frequency interference, thereby enhancing the performance of the search pipeline.