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AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size
To substantiate the efficacy of our method, we experimentally show, how the introduction of adaptive step size and adaptive batch size gradually improves the performance of regular SGD.
Learning Confident Classifiers in the Presence of Label Noise
We evaluate the proposed method on a series of classification tasks such as noisy versions of MNIST, CIFAR-10, Fashion-MNIST datasets as well as CIFAR-10N, which is real-world dataset with noisy human annotations.
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