Data Augmentation by Image-to-Image Translation for Image Retrieval

Daytime and nighttime visual appearance changes are addressed with artificially learned data augmentation. Convolutional neural networks (CNNs) are one of the state-of-the-art techniques for image retrieval. However, powerful deep neural networks are data-driven resulting in poor performance, when an irregular query, different from training data, is inputted. Augmentation is addressed with pix2pix a CycleGAN, used to provide image-to-image translation from regular daytime images into irregular nighttime images and are trained over four image datasets. To measure image translation quality, Generative Adversarial Network (GAN) evaluation scores are explored and compared with data augmentation. The final data augmentation effect is tested on the image retrieval benchmarks, where results show improvement on the 24/7 Tokyo dataset with minor performance loss on daytime Revisited Oxford and Paris datasets.

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