A deep learning loss based on additive cosine margin: Application to fashion style and face recognition

Abstract

Recently, loss functions based on angular spans improved the performance of deep visual recognition. These losses converted Euclidean cross entropy to angular cross entropy loss. Fashion style recognition deals with the problem of assigning a person’s outfit to a fashion style category. Due to the high similarity between different clothing items and the use of softmax-based loss functions, many of the current methods that address this problem show relatively poor performance and cannot guarantee sufficient inter-class margins in the fashion domain. In this work, we propose an end-to-end method for deep visual recognition by combining a standard CNN architecture with a novel loss function, which we call Additive Cosine Margin Loss (ACML). The proposed function not only projects feature vectors of different classes into different regions of the embedding, but also enforces compactness of the projections within each class. Our experiments were conducted on two public and well-known fashion style recognition datasets FashionStyle14 and HipsterWars, and on the face verification and identification datasets LFW, YTF, and MegaFace. These experiments demonstrate the superiority of the proposed loss function over: (1) existing angular margin-based loss functions (2) state-of-the-art methods for clothing style recognition as well as face analysis tasks.