Decorrelation-Based Self-Supervised Visual Representation Learning for Writer Identification

Self-supervised learning has developed rapidly over the last decade and has been applied in many areas of computer vision. Decorrelation-based self-supervised pretraining has shown great promise among non-contrastive algorithms, yielding performance at par with supervised and contrastive self-supervised baselines. In this work, we explore the decorrelation-based paradigm of self-supervised learning and apply the same to learning disentangled stroke features for writer identification. Here, we propose a modified formulation of the decorrelation-based framework named SWIS which was proposed for signature verification by standardizing the features along each dimension on top of the existing framework. We show that the proposed framework outperforms the contemporary self-supervised learning framework on the writer identification benchmark by 0.89%, 0.56%, and 1.23% on word-level images and 1.15%, 0.10%, and 0.39% on page-level images on IAM, CVL ,and Firemaker datasets, respectively. The proposed framework achieves word level accuracy of 87.94%, 84.80%, 93.32%, 74.24% and page level accuracy of 97.09%, 95.58%, 96.87%, 98.40% on AHAWP, IAM, CVL and Firemaker datasets, respectively, outperforming several recent supervised methods as well.