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arXiv Machine Learning By Hannan Ejaz Keen, Muhammad Moazam Fraz, Karsten Berns

Beyond Pixel Similarity: Task-Aware Evaluation of GAN-Based Synthetic Sonar Data for Robotic Perception

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The paper examines whether standard image‑fidelity metrics (SSIM, PSNR, MSE) accurately reflect the performance of GAN‑generated synthetic sonar data in robotic perception tasks. Using a Pix2Pix GAN with four discriminator configurations (PixelGAN, PatchGAN‑16, PatchGAN‑70, ImageGAN), the authors train object detectors (YOLOX‑S, YOLOX‑L, Faster R‑CNN) solely on real sonar images and evaluate them on the synthetic outputs. Results show a mismatch: the discriminator that yields the best pixel‑level scores does not always produce the best detection performance, with PatchGAN models achieving strong downstream results despite lower SSIM/PSNR/MSE values.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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