Correcting Corrupted Labels Using Mode Dropping of ACGAN

Machine learning often requires a large amount of training data, and the training data obtained from various sources is often of poor quality, such as a large number of corrupted labels. Researchers using machine learning often apply some data cleaning techniques to clean up corrupted data. There are two popular methods to clean corrupted data: one is to set manual cleaning rules, and the other is to use positive samples for machine learning or statistical methods. Our work proposes a data cleaning method based on ACGAN since it is difficult to manually formulate cleaning rules, and there are often no positive samples of training data too. Our work does not need to artificially add cleaning rules or positive samples, and subtly uses mode dropping of GAN to eliminate the impact of noisy labels on corrupted data so which can be converted to relatively clean synthetic training data. Mode dropping of ACGAN will naturally happens, which is originally a disadvantage that usually needs to be eliminated in GAN, we tom the disadvantage into advantage, ACGAN will ignore some non-subject features when generating data, so as to eliminate the impact of noisy labels. And we also apply our method to correct noisy labels on corrupted training data.