CVPR 2023 - Equiangular Basis Vectors

In this episode we discuss Equiangular Basis Vectors by Yang Shen, Xuhao Sun, Xiu-Shen Wei. This paper proposes a new approach for classification tasks, called Equiangular Basis Vectors (EBVs), which generate normalized vector embeddings as "predefined classifiers". These vectors are required to be equal in status and as orthogonal as possible. By minimizing the spherical distance between the embedding of an input and its categorical EBV during training, predictions are made by identifying the EBV with the smallest distance during inference. The method outperforms fully connected classifiers on the ImageNet-1K dataset and other tasks, and does not significantly increase computation compared to classical metric learning methods.