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Regularization is essential for avoiding over-fitting to training data innetwork optimization, leading to better generalization of the trained networks.The label noise provides a strong implicit regularization by replacing thetarget ground truth labels of training examples by uniform random labels.However, it can cause undesirable misleading gradients due to the large lossassociated with incorrect labels. We propose a first-order optimization method(Label-Noised Trim-SGD) that uses the label noise with the example trimming inorder to remove the outliers based on the loss. The proposed algorithm issimple yet enables us to impose a large label-noise and obtain a betterregularization effect than the original methods. The quantitative analysis isperformed by comparing the behavior of the label noise, the example trimming,and the proposed algorithm. We also present empirical results that demonstratethe effectiveness of our algorithm using the major benchmarks and thefundamental networks, where our method has successfully outperformed thestate-of-the-art optimization methods.

Regularization in network optimization via trimmed stochastic gradient descent with noisy label