Bayesian Metric Learning for Robust Training of Deep Models under Noisy Labels
Label noise is a natural event of data collection and annotation and has been shown to have significant impact on the performance of deep learning models regarding accuracy reduction and sample complexity increase. This paper aims to develop a novel theoretically sound Bayesian deep metric learning that is robust against noisy labels. Our proposed approach is inspired by a linear Bayesian large margin nearest neighbor classification, and is a combination of Bayesian learning, triplet loss-based deep metric learning and variational inference frameworks. We theoretically show the robustness under label noise of our proposed method. The experimental results on benchmark data sets that contain both simulated and realistic label noise show a considerable improvement in the classification accuracy of our method compared to the linear Bayesian metric learning and the point estimate deep metric learning.
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MNIST
MS-Celeb-1M
