Boosting Standard Classification Architectures Through a Ranking Regularizer

Standard classification architectures (e.g, ResNet and DesneNet) achieve great performance. However, they can not answer the following question: What is the nearest neighbor image to a given query image? This question reveals an underlying limitation of the softmax loss. The softmax loss, used in training classification models, is prone to overfitting. It achieves superior classification performance, yet with an inferior class embedding. To address this limitation, recent literature [2,3] assumes a fixed number of modes per…