A novel multi-scale loss function for classification problems in machine learning
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Abstract (arXiv)
We introduce two-scale loss functions for use in various gradient descent algorithms applied to classification problems via deep neural networks. This new method is generic in the sense that it can be applied to a wide range of machine learning architectures, from deep neural networks to support vector machines for example. These two-scale loss functions allow to focus the training onto objects in the training set which are not well classified. This leads to an increase in several measures of performance for appropriately-defined two-scale loss functions with respect to the more classical cross-entropy when tested on traditional deep neural networks on the MNIST, CIFAR10, and CIFAR100 data-sets.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 4 Jun 2021.
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References · 9
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 994
- 2016From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classificationcited by 337
- 2021ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networkscited by 293
- 2019On The Power of Curriculum Learning in Training Deep Networkscited by 285
- 2015An Exploration of Softmax Alternatives Belonging to the Spherical Loss Familycited by 27
- 2020Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Functioncited by 26
- 2018Taming the Cross Entropy Losscited by 10
- 2020Stability for the Training of Deep Neural Networks and Other Classifierscited by 3
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