MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels

• URL: http://arxiv.org/abs/2103.10869v1
• Date: Fri, 19 Mar 2021 15:47:44 GMT
• Title: MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels
• Authors: G\"orkem Algan, Ilkay Ulusoy
• Abstract summary: Real-world datasets commonly have noisy labels, which negatively affects the performance of deep neural networks (DNNs) We propose a label noise robust learning algorithm, in which the base classifier is trained on soft-labels that are produced according to a meta-objective. Our algorithm uses a small amount of clean data as meta-data, which can be obtained effortlessly for many cases.
• Score: 0.20305676256390928
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Real-world datasets commonly have noisy labels, which negatively affects the performance of deep neural networks (DNNs). In order to address this problem, we propose a label noise robust learning algorithm, in which the base classifier is trained on soft-labels that are produced according to a meta-objective. In each iteration, before conventional training, the meta-objective reshapes the loss function by changing soft-labels, so that resulting gradient updates would lead to model parameters with minimum loss on meta-data. Soft-labels are generated from extracted features of data instances, and the mapping function is learned by a single layer perceptron (SLP) network, which is called MetaLabelNet. Following, base classifier is trained by using these generated soft-labels. These iterations are repeated for each batch of training data. Our algorithm uses a small amount of clean data as meta-data, which can be obtained effortlessly for many cases. We perform extensive experiments on benchmark datasets with both synthetic and real-world noises. Results show that our approach outperforms existing baselines.

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