A complete survey on generative ai (aigc): Is chatgpt from gpt-4 to gpt-5 all you need?
As ChatGPT goes viral, generative AI (AIGC, aka AI-generated content) has made headlines
everywhere because of its ability to analyze and create text, images, and beyond. With such …
everywhere because of its ability to analyze and create text, images, and beyond. With such …
A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …
computational technology as well as engineering tools in material modeling and material …
Deep image deblurring: A survey
Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …
sharp image from a blurred input image. Advances in deep learning have led to significant …
Towards real-world blind face restoration with generative facial prior
Blind face restoration usually relies on facial priors, such as facial geometry prior or
reference prior, to restore realistic and faithful details. However, very low-quality inputs …
reference prior, to restore realistic and faithful details. However, very low-quality inputs …
Sparse gradient regularized deep retinex network for robust low-light image enhancement
Due to the absence of a desirable objective for low-light image enhancement, previous data-
driven methods may provide undesirable enhanced results including amplified noise …
driven methods may provide undesirable enhanced results including amplified noise …
Grid search, random search, genetic algorithm: a big comparison for NAS
P Liashchynskyi, P Liashchynskyi - arXiv preprint arXiv:1912.06059, 2019 - arxiv.org
In this paper, we compare the three most popular algorithms for hyperparameter
optimization (Grid Search, Random Search, and Genetic Algorithm) and attempt to use them …
optimization (Grid Search, Random Search, and Genetic Algorithm) and attempt to use them …
Deep stacked hierarchical multi-patch network for image deblurring
Despite deep end-to-end learning methods have shown their superiority in removing non-
uniform motion blur, there still exist major challenges with the current multi-scale and scale …
uniform motion blur, there still exist major challenges with the current multi-scale and scale …
Generative image inpainting with contextual attention
Recent deep learning based approaches have shown promising results for the challenging
task of inpainting large missing regions in an image. These methods can generate visually …
task of inpainting large missing regions in an image. These methods can generate visually …
Deblurgan: Blind motion deblurring using conditional adversarial networks
O Kupyn, V Budzan, M Mykhailych… - Proceedings of the …, 2018 - openaccess.thecvf.com
We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning
is based on a conditional GAN and the content loss. DeblurGAN achieves state-of-the art …
is based on a conditional GAN and the content loss. DeblurGAN achieves state-of-the art …
Learning deep CNN denoiser prior for image restoration
Abstract Model-based optimization methods and discriminative learning methods have been
the two dominant strategies for solving various inverse problems in low-level vision …
the two dominant strategies for solving various inverse problems in low-level vision …