Towards Discriminability and Diversity: Batch Nuclear-Norm Maximization Under Label Insufficient Situations
Towards Discriminability and Diversity: Batch Nuclear-Norm Maximization Under Label Insufficient Situations
The learning of the deep networks largely relies on the data with human-annotated labels. In some label insufficient situations, the performance degrades on the decision boundary with high data density. A common solution is to directly minimize the Shannon Entropy, but the side effect caused by entropy minimization, \it i.e., …