Applications such as autonomous vehicles and medical screening use deep learning models to localize and identify hundreds of objects in a single frame. In the past, it has been shown how an attacker can fool these models by placing an adversarial patch w
Adversarial Adventures: Fixed an issue where unsummoning Azandar while scrying for fates would prevent you from progressing. Paths Unwalked: Polished the beginning sequences of the quest in Azandar’s Sanctum. The description for the quest item “Arcanomystical Stencil” now more closely resemble...
In this study, a novel deep perceptual patch generative adversarial network (FDPPGAN) was proposed to solve the pan-sharpening problem. First, a perception generator was constructed, it included, a matching module, which can process as input images of different resolutions, a fusion module, a ...
Objective no-reference image- and video-quality metrics are crucial in many computer vision tasks. However, state-of-the-art no-reference metrics have become learning-based and are vulnerable to adversarial attacks. The vulnerability of quality metrics imposes restrictions on using such metrics in ...
2.Statistics based mehodsmatch the disribution of input and target with aGaussian model. 3. Adversarial training(GANs)can recognize such manifoldwith itsdiscriminative network. andstrengthen its generativepower with a projection on the manifold. ...
the training manager module128uses the WGAN adversarial losses with the weighted sum of pixelwise l1loss. In one or more implementations, the training manager module128compares the outputs of the global and local critics316,318using a Wasserstein-1 distance in WGAN, which is based on discounted ...
In this study, a novel deep perceptual patch generative adversarial network (FDPPGAN) was proposed to solve the pan-sharpening problem. First, a perception generator was constructed, it included, a matching module, which can process as input images of different resolutions, a fusion module, a ...
Tensor Factorization-based Particle Swarm Optimization for Large-Scale Many-Objective Problems. Swarm Evol. Comput. 2021, 69, 100995. [Google Scholar] [CrossRef] Chen, Y.; Guo, Y.; Wang, Y.; Dong, W.; Chong, P.; He, G. Denoising of Hyperspectral Images Using Nonconvex Low Rank ...
Most previous approaches are based on supervised learning, which requires a large-scale labeled dataset. However, such large-scale annotated datasets for fine-grained image recognition are difficult to collect because they generally require domain expertise during the labeling process. In this study, ...
Table 1. Description of matrix-based LRSD methods. Table 1. Description of matrix-based LRSD methods. MethodAuthorsObjectiveContribution Infrared patch-image (IPI) [25] Gao et al. Constructs low-rank sparse matrices Effective with smoother backgrounds Re-weighted infrared patch-image (WIPI) [26,27...