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Label smoothing loss is a widely adopted technique to mitigate overfitting in deep neural networks.
Approximation by superpositions of a sigmoidal function
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Imagenet: A large-scale hierarchical image database
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The expressive power of neural networks: A view from the width
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Provable approximation properties for deep neural networks
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
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Does label smoothing mitigate label noise?
Michal Lukasik, Srinadh Bhojanapalli, Aditya Menon, and Sanjiv Kumar · 2020
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Neural collapse with unconstrained features
Dustin G Mixon, Hans Parshall, and Jianzong Pi · 2020
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Prevalence of neural collapse during the terminal phase of deep learning training
Vardan Papyan, XY Han, and David L Donoho · 2020
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Explicit regularization and implicit bias in deep network classifiers trained with the square loss
Tomaso Poggio and Qianli Liao · 2020
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Imbalance trouble: Revisiting neural-collapse geometry
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Extended unconstrained features model for exploring deep neural collapse
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Numerical linear algebra , volume 181
Lloyd N Trefethen and David Bau · 2022
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Neural collapse with normalized features: A geometric analysis over the riemannian manifold
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Continual learning by modeling intra-class variation
Longhui Yu, Tianyang Hu, Lanqing Hong, Zhen Liu, Adrian Weller, and Weiyang Liu · 2022
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Neural collapse in deep linear networks: from balanced to imbalanced data
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Neural collapse for unconstrained feature model under cross-entropy loss with imbalanced data
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Generalized neural collapse for a large number of classes
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Inducing neural collapse in deep long-tailed learning
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Perturbation analysis of neural collapse
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