Uncertainty-aware Dynamic Re-weighting Robust Classification

In recent years, deep neural networks (DNNs) have achieved significant success in a variety of computer vision tasks. However, data collected from the wild inevitably suffer from noise, impairing the performance of neural networks. Moreover, due to the diversity of noise, it is impossible to predetermine the types of noise in test time, making traditional methods for handling single noise inadequate. In order to train reliable networks for noisy data, we present a novel framework to systematically address complex noise issues in the real world. Specifically, we propose an uncertainty-aware dynamic re-weighting classification framework. Focusing on the quality and difficulty of samples, our method categorizes samples into easy-to-learn, challenging and unclassifiable samples. Introducing uncertainty to distinguish among these categories, our method dynamically adjusts the weights of samples to prevent the network from overfitting noisy data. Finally, we validate the proposed method on both synthetic and real-world datasets, demonstrating advanced performance in classification tasks.