Multi-head enhanced self-attention network for novelty detection

• Published in: Pattern recognition 2020-11, Vol.107, p.107486, Article 107486
• Main Authors: Zhang, Yingying, Gong, Yuxin, Zhu, Haogang, Bai, Xiao, Tang, Wenzhong
• Format: Article
• Language: English
• Subjects: Adversarial Learning; Adversarial-balance loss; Multihead attention network; Multihead enhanced self-attention; One-class classification
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2020.107486

•The title is “MANet: Multi-head enhanced self-attention network for novelty detection”.•We have modified the conclusion part in the manuscript. Please see Section 5 in the manuscript.•We have already modified the reference in the manuscript. One-class classification (OCC) is a classical problem in computer vision that can be described as the task of classifying outlier class samples (OC samples) from the OCC model trained on inlier class samples (IC samples) when datasets are highly biased toward one class due to the insufficient sample size of the other class. Currently, the adversarial learning OCC (ALOCC) method has been proven to significantly improve OCC performance. However, its drawbacks include instability issues and non-evident reconstruction between the IC and OC samples. Therefore, we propose multihead enhanced self-attention in the ALOCC network, thereby increasing the difference between the IC and OC samples and significantly increasing OCC accuracy compared with ALOCC accuracy. For training, we propose a new loss, called adversarial-balance loss, that effectively solves the training instability problem, further increasing OCC accuracy. The experiments show the effectiveness of the proposed method compared with state-of-art methods.