Effect of Reconstruction Losses in Discriminative and Generative Learning based Networks for the Person Re-identification

Abstract

The Person Re-identification (Re-ID) task has gained popularity in recent times. Researchers are continuously looking to improve the accuracy of the existing person Re-ID systems. Identifying the person from the surveillance footage can be essential to security concerns. Currently, there are many state-of-art Person Re-ID systems available. Deep learning frameworks are also adopted for designing Re-ID systems. Apart from deep learning-based approaches, the Generative Adversarial Networks (GAN) based approach also gained substantial interest in Person Re-ID tasks. Augmentation of training data has significantly improved the performance of the system. Our primary objective is to analyze the effect of applying different reconstruction losses and their combinations on the GAN-based approach. The Discriminative and Generative Learning (DG-Net) approach is chosen for carrying out this study from other existing GAN-based systems. DG-Net is currently considered benchmarked in the GAN-based method for person Re-ID. Analysis shows that the proposed idea of using a variety of reconstruction losses simultaneously significantly improves the existing system's performance. Using the proposed technique of fusing multiple Losses simultaneously, we achieved a massive performance gain of 20.57% over the current benchmarked approach on the Market1501 dataset. This paper includes a thorough study of different loss functions and their effect on the generated images for performing Person Re-ID tasks.