Equiangular Basis Vectors: A Novel Paradigm for Classification Tasks

In this paper, we propose Equiangular Basis Vectors (EBVs) as a novel training paradigm of deep learning for image classification tasks. Differing from prominent training paradigms, e.g., k-way classification layers (mapping the learned representations to the label space) and deep metric learning (quantifying sample similarity), our method generates normalized vector embeddings as "predefined classifiers", which act as the fixed learning targets corresponding to different categories. By minimizing the spherical distance of the embedding of an input between its categorical EBV in training, the predictions can be obtained by identifying the categorical EBV with the smallest distance during inference. More importantly, by directly adding EBVs corresponding to newly added categories of equal status on the basis of existing EBVs, our method exhibits strong scalability to deal with the large increase of training categories in open-environment machine learning. In experiments, we evaluate EBVs on diverse computer vision tasks with large-scale real-world datasets, including classification on ImageNet-1K, object detection on COCO, semantic segmentation on ADE20K, etc. We further collected a dataset consisting of 100,000 categories to validate the superior performance of EBVs when handling a large number of categories. Comprehensive experiments validate both the effectiveness and scalability of our EBVs. Our method won the first place in the 2022 DIGIX Global AI Challenge, code along with all associated logs are open-source and available at https://github.com/aassxun/Equiangular-Basis-Vectors.