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.
arXiv:2606. 25128v1 Announce Type: cross Abstract: Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs.
arXiv:2606. 28416v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have shown outstanding performance in visual recognition tasks within vision sensor networks; however, they are still vulnerable to adversarial manipulations and imperceptible perturbations that can lead to erroneous predictions.
The paper investigates Automatic Target Recognition (ATR) in Synthetic Aperture Sonar (SAS) imagery, comparing modern convolutional neural networks (CNNs) and transformer-based deep neural networks (DNNs). It examines how factors such as network size, architecture, pretraining methods, data augmentation, and regularization influence performance, aiming to identify the highest-performing model and provide a training roadmap for state‑of‑the‑art SAS‑ATR systems.
Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs. Synthetic data augmentation can extend existing datasets with realistic images, and the quality of these images is generally assessed through fidelity metrics such as FID, KID, IS, LPIPS and SSIM that measure structural or distributional similarity.
arXiv:2607. 23770v1 Announce Type: new Abstract: In this work we study Automatic Target Recognition (ATR) for Synthetic Aperture Sonar (SAS) data with a focus on deep neural networks (DNNs).
EmbeddGAN introduces a new GAN framework that replaces the traditional discriminator with an embedding network trained to maximize statistical dependence between embeddings and real/fake labels using Gini distance correlation (gCor). The generator simultaneously minimizes this dependence, encouraging real and generated samples to become indistinguishable in the learned low‑dimensional embedding space. Experiments on MNIST, CIFAR‑10, and CelebA show competitive performance and notably more stable training dynamics compared to established baselines.
arXiv:2608. 04720v4 Announce Type: replace Abstract: Real-time object detectors achieve remarkable accuracy under controlled conditions, yet degrade sharply on non-ideal inputs-fisheye distortion, game-rendered content, aerial views, and 360{\deg}panoramas.
arXiv:2608.22072v1 Announce Type: new Abstract: Autonomous underwater vehicles (AUVs) are increasingly important tools in industries ranging from research, to energy, to defense. AUVs are power-const...
arXiv:2512.17730v2 Announce Type: replace Abstract: Detectors of AI-generated images tend to inherit the biases of the data they are trained on: models fitted to GAN imagery learn to treat GAN-specif...
arXiv:2510. 06596v2 Announce Type: replace-cross Abstract: The performance of machine learning models depends heavily on training data.
SonarLLM is a multimodal large language model that treats sonar as a native perceptual modality, combining a sonar‑specific encoder, physics‑aware feature enhancement, and reliability‑aware hierarchical fusion to align acoustic structure with optical semantics. The authors introduce SonarBench, a benchmark covering recognition, counting, visual question answering, and captioning across sonar‑only, optical‑only, and fusion settings, enabling controlled measurement of cross‑modal complementarity. SonarLLM achieves 72.0% macro accuracy on sonar‑only tasks and 68.7% under fusion, outperforming baselines by significant margins and demonstrating increasing fusion gains as optical visibility degrades.
The paper introduces a GAN‑based semantic communication framework for image transmission in the Internet of Vehicles, aiming to overcome bandwidth and channel limitations. At the transmitter, a pyramid attention network extracts semantic label maps and a priority mechanism assigns weights to categories based on driving safety, guiding bit allocation and loss design. The receiver reconstructs images using a coarse‑to‑fine multi‑resolution generator, multi‑scale discriminator, temporal consistency, spatial pyramid pooling, and class‑aware convolutions, achieving high‑fidelity results with combined adversarial, feature‑matching, and perceptual losses. Experiments on Cityscapes demonstrate superior semantic segmentation accuracy and image quality compared to existing methods, with stable performance under AWGN and Rayleigh channels.