# Improved Mix-up with KL-Entropy for Learning From Noisy Labels Source: [Improved Mix-up with KL-Entropy for Learning From Noisy Labels]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:44:30.600Z Authors: Qian Zhang, Feifei Lee, Ya-Gang Wang, Qiu Chen Published: 2019-08-15T10:55:37.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()