Multi-head Siamese Prototype Learning against both Data and Label Corruption
Peng-Fei Zhang, Zi Helen Huang · 2023
The training of the Deep Neural Network (DNN) has been seriously challenged by insidious noise in the dataset, including noise in raw data and errors in annotations. Existing methods usually limit their efforts to the defense of one particular kind of noise, which would be powerless when facing the coexistence of various noise. To deal with it, we propose a novel Multi-head Siamese Prototype Learning (MSPL) method to promote discriminative features and representative prototypes by modeling invariance in samples and sieving out incorrectness in labels. More specifically, a multi-head Siamese network structure is constructed, where prototype learning with the multi-consistency constraint is performed to improve the resilience of the model to noise. Under this regime, adversarial contrastive learning is performed to train the model with the dynamically generated vicious adversarial examples, further enhancing the invariant predictive ability against data noise. At the same time, to deal with label noise, an effective multi-granularity sample selection strategy is designed to filter out noisy labels by measuring the error distribution in both global and local (i.e., class-specific) perspectives. Semi-supervised learning is accordingly conducted to train the model with the resulting labelled data (i.e., data with clean labels) and unlabelled data (i.e., data with noisy labels). Extensive experiments on benchmarks demonstrate the effectiveness of the proposed method in the extremely noisy learning environment.