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Face recognition has made tremendous progress in recent years due to the advances in loss functions and the explosive growth in training sets size. A properly designed loss is seen as key to extract discriminative features for classification. ...

Training datasets for deep models inevitably contain noisy labels, such labels can seriously impair the performance of deep models. Empirically, all labels will be remembered after enough epochs, while pure labels will be remembered first and then ...

Hepatectomy is currently one of the most commonly used treatment methods for malignant liver tumors. It is of great significance to clinical surgery to perform accurate hepatic vessel segmentation in preoperative CT images. However, due to the complex ...

Noise label exists widely in real-world data, resulting in the degradation of classification performance. Popular methods require a known noise distribution or additional cleaning supervision, which is usually unavailable in practical ...

Fisher discriminant analysis (FDA) is a popular and powerful method for dimensionality reduction and classification. Unfortunately, the optimality of the dimension reduction provided by FDA is only proved in the homoscedastic case. In addition, FDA is ...