16 citations · 31 across the 16 of their papers we have counts for
4 papers · 1 filter
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination
Tingting Wu, Xiao Ding, Hao Zhang +4
Given data with label noise (i.e., incorrect data), deep neural networks would gradually memorize the label noise and impair model performance. To relieve this issue, curriculum le…
Randomized Sharpness-Aware Training for Boosting Computational Efficiency in Deep Learning
Yang Zhao, Hao Zhang, Xiuyuan Hu
By driving models to converge to flat minima, sharpness-aware learning algorithms (such as SAM) have shown the power to achieve state-of-the-art performances. However, these algori…
Neighborhood Region Smoothing Regularization for Finding Flat Minima In Deep Neural Networks
Yang Zhao, Hao Zhang
Due to diverse architectures in deep neural networks (DNNs) with severe overparameterization, regularization techniques are critical for finding optimal solutions in the huge hypot…
Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning
Yang Zhao, Hao Zhang, Xiuyuan Hu
How to train deep neural networks (DNNs) to generalize well is a central concern in deep learning, especially for severely overparameterized networks nowadays. In this paper, we pr…