CUB-200-2011

Diversity-Aware Prototype Learning (DAPL), a prototype-based neural network method that enforces prototype diversity through architectural constraints instead of explicit regularization, achieved 81.6…

Researchers propose a loss-aware feature-map pruning framework for convolutional neural networks that uses multi-armed bandits (UCB1 and Thompson Sampling) to identify and remove redundant feature map…

Researchers propose vMFProto, a distributional part-prototype framework that models each class as a mixture of von Mises-Fisher components on the hypersphere for interpretable classification. The meth…