Related papers: Learning with Noisy Labels through Learnable Weighting and Centroid Similarity

Learning with Noisy Labels through Learnable Weighting and Centroid Similarity

URL: http://arxiv.org/abs/2303.09470v2
Date: Tue, 25 Jun 2024 14:36:33 GMT
Title: Learning with Noisy Labels through Learnable Weighting and Centroid Similarity
Authors: Farooq Ahmad Wani, Maria Sofia Bucarelli, Fabrizio Silvestri,
Abstract summary: noisy labels are prevalent in domains such as medical diagnosis and autonomous driving. We introduce a novel method for training machine learning models in the presence of noisy labels. Our results show that our method consistently outperforms the existing state-of-the-art techniques.
Score: 5.187216033152917
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We introduce a novel method for training machine learning models in the presence of noisy labels, which are prevalent in domains such as medical diagnosis and autonomous driving and have the potential to degrade a model's generalization performance. Inspired by established literature that highlights how deep learning models are prone to overfitting to noisy samples in the later epochs of training, we propose a strategic approach. This strategy leverages the distance to class centroids in the latent space and incorporates a discounting mechanism, aiming to diminish the influence of samples that lie distant from all class centroids. By doing so, we effectively counteract the adverse effects of noisy labels. The foundational premise of our approach is the assumption that samples situated further from their respective class centroid in the initial stages of training are more likely to be associated with noise. Our methodology is grounded in robust theoretical principles and has been validated empirically through extensive experiments on several benchmark datasets. Our results show that our method consistently outperforms the existing state-of-the-art techniques, achieving significant improvements in classification accuracy in the presence of noisy labels. The code for our proposed loss function and supplementary materials is available at https://github.com/wanifarooq/NCOD

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