Deep Self-Supervised Diversity Promoting Learning on Hierarchical Hyperspheres for Regularization

In this paper, we propose a novel approach to enhance the generalization performance of deep neural networks. Our method employs a hierarchical hypersphere-based constraint that organizes weight vectors hierarchically based on observed data. By diversifying the parameter space of hyperplanes in the...

Full description