Collaborative Label Correction via Entropy Thresholding Deep neural networks (DNNs) have the capacity to fit extremely noisy labels nonetheless they tend to learn data with clean labels first and then memorize those with noisy labels. We examine this behavior in light of the Shannon entropy of the predictions and demonstrate the low entropy predictions determined by a given threshold are much more reliable as the supervision than the original noisy labels. It also shows the advantage in maintaining more training samples than previous methods. Then, we power this entropy criterion with the Collaborative Label Correction (CLC) framework to further avoid undesired local minimums of the single network. A range of experiments have been conducted on multiple benchmarks with both synthetic and realworld settings. Extensive results indicate that our CLC outperforms several stateoftheart methods.

Generative Correlation Discovery Network for Multilabel Learning The goal of Multilabel learning is to predict multiple labels of each single instance. This is a challenging problem since the training data is limited, longtail label distribution, and complicated label correlations. Generally, more training samples and label correlation knowledge would benefit the learning performance. However, it is difficult to obtain largescale welllabeled datasets, and building such a label correlation map requires sophisticated semantic knowledge. To this end, we propose an endtoend Generative Correlation Discovery Network (GCDN) method for multilabel learning in this paper. GCDN captures the existing data distribution, and synthesizes diverse data to enlarge the diversity of the training features; meanwhile, it also learns the label correlations based on a specificallydesigned, simple but effective correlation discovery network to automatically discover the label correlations and considerately improve the label prediction accuracy. Extensive experiments on several benchmarks are provided to demonstrate the effectiveness, efficiency, and high accuracy of our approach.