Improved Mix up with KL Entropy for Learning From Noisy Labels
Despite the deep neural networks (DNN) has achieved excellent performance in image classification researches, the training of DNNs needs a large of clean data with accurate annotations. The collect of a dataset is easy, but it is difficult to annotate the collecting data. On the websites, there exist a lot of image data which contains inaccurate annotations, but training on these datasets may make networks easier to over fit the noisy labels and cause performance degradation. In this work, we propose an improved joint optimization framework, which mixed the mix up entropy and Kullback Leibler (KL) entropy as the loss function. The new loss function can give the better fine tuning after the framework updates both the label annotations. We conduct experiments on CIFAR 10 dataset and Clothing1M dataset. The result shows the advantageous performance of our approach compared with other state of the art methods.
ArXiv