Transductive Centroid Projection for Semi-Supervised Large-Scale Recognition

Abstract

Conventional deep semi-supervised learning methods, such as recursive clustering and training process, suffer from cumulative error and high computational complexity when collaborating with Convolutional Neural Networks. To this end, we design a simple but effective learning mechanism that merely substitutes the last fully-connected layer with the proposed Transductive Centroid Projection (TCP) module. It is inspired by the observation of the weights in classification layer (called extit{anchors}) converge to the central direction of each class in hyperspace. Specifically, we design the TCP module by dynamically adding an extit{ad hoc anchor} for each cluster in one mini-batch. It essentially reduces the probability of the inter-class conflict and enables the unlabelled data functioning as labelled data. We inspect its effectiveness with elaborate ablation study on seven public face/person classification benchmarks. Without any bells and whistles, TCP can achieve significant performance gains over most state-of-the-art methods in both fully-supervised and semi-supervised manners.