SAMN applies the Pool Adjacent Violators Algorithm to directly enforce monotonicity on per-class weight norms during classifier retraining, eliminating hyperparameters while integrating with other long-tailed methods.
Identifying and compensating for feature deviation in imbalanced deep learning.arXiv preprint arXiv:2001.01385,
2 Pith papers cite this work. Polarity classification is still indexing.
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Class imbalance causes DNNs to underfit minority classes early in training and produce non-generalizable minority representations later by overfitting to minimize overall loss.
citing papers explorer
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Why Not Hyperparameter-Friendly Optimisation? A Monotonic Adaptive Norm Rescaling Approach For Long-Tailed Recognition
SAMN applies the Pool Adjacent Violators Algorithm to directly enforce monotonicity on per-class weight norms during classifier retraining, eliminating hyperparameters while integrating with other long-tailed methods.
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On the Impact of Class Imbalance on the Learning Dynamics of Deep Neural Networks:An Intuitive Insight
Class imbalance causes DNNs to underfit minority classes early in training and produce non-generalizable minority representations later by overfitting to minimize overall loss.