Gp-Gan

Wu, Huikai; Zheng, Shuai; Zhang, Junge; Huang, Kaiqi

doi:10.1145/3343031.3350944

Cited by 163 publications

(20 citation statements)

References 18 publications

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“…Based on GAN, many algorithms have been developed, such as conditional GAN (Mirza & Osindero, ), StackGAN (Zhang et al, ), and GP‐GAN (Wu, Zheng, Zhang, & Huang, ). In particular, adversarial training has been used for generating realistic text (Zhang et al, ).…”

Section: Deep Learningmentioning

confidence: 99%

From shallow feature learning to deep learning: Benefits from the width and depth of deep architectures

Zhong

Ling

Wang

2018

WIREs Data Min & Knowl

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Since Pearson developed principal component analysis (PCA) in 1901, feature learning (or called representation learning) has been studied for more than 100 years. During this period, many “shallow” feature learning methods have been proposed based on various learning criteria and techniques, until the popular deep learning research in recent years. In this advanced review, we describe the historical profile of the shallow feature learning research and introduce the important developments of the deep learning models. Particularly, we survey the deep architectures with benefits from the optimization of their width and depth, as these models have achieved new records in many applications, such as image classification and object detection. Finally, several interesting directions of deep learning are presented and briefly discussed. This article is categorized under: Technologies > Classification Technologies > Machine Learning Technologies > Computational Intelligence

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Section: Deep Learningmentioning

confidence: 99%

From shallow feature learning to deep learning: Benefits from the width and depth of deep architectures

Zhong

Ling

Wang

2018

WIREs Data Min & Knowl

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“…More recently, various learning-based methods have been proposed, including blending deep features instead of pixels [22,32,81] or designing loss functions based on deep features [95,96]. Generative Adversarial Networks (GAN) have also been used for image blending [10,23,47,77,88,101,105]. For example, In-DomainGAN [101] exploits GAN inversion to achieve seamless blending, and StyleMap-GAN [47] blends images in the spatial latent space.…”

Section: Related Workmentioning

confidence: 99%

“…However, blending images seamlessly requires delicate adjustment of color, texture, and shape, often requiring users' expertise and tedious manual processes. To reduce human efforts, researchers have proposed various automatic image blending algorithms, including classic methods [9,55,70,84] and deep neural networks [61,88,102].…”

Section: Introductionmentioning

confidence: 99%

College students’ perceptions of AI-based writing learning tools: With a focus on Google Translate, Naver Papago, and Grammarly

Kim¹,

Han²

2021

meeso

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Image blending aims to combine multiple images seamlessly. It remains challenging for existing 2D-based methods, especially when input images are misaligned due to differences in 3D camera poses and object shapes. To tackle these issues, we propose a 3D-aware blending method using generative Neural Radiance Fields (NeRF), including two key components: 3D-aware alignment and 3D-aware blending. For 3D-aware alignment, we first estimate the camera pose of the reference image with respect to generative NeRFs and then perform 3D local alignment for each part. To further leverage 3D information of the generative NeRF, we propose 3D-aware blending that directly blends images on the NeRF's latent representation space, rather than raw pixel space. Collectively, our method outperforms existing 2D baselines, as validated by extensive quantitative and qualitative evaluations with FFHQ and AFHQ-Cat.

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“…The networks are quickly extended and deployed in many applications. [28,[33][34][35][36] GAN configures two adversaries: a discriminator and a generator, both are typically modeled as deep neural networks. Both discriminator and generator are trained in parallel.…”

Section: Ganmentioning

confidence: 99%

Deep Learning for Medical Materials: Review and Perspective

Qi¹,

Liu²

2021

ES Mater. Manuf.

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Deep learning and similar computational research approaches have cooperated with materials research for years. Especially in the last few years, with the fast evolution of machine learning and deep learning algorithms, a novel branch for material research is presented to be recognized, learned, practiced, adapted and perfected. Different conventional computational modeling methods, the deep learning approach assists material science and engineering from the aspect of data processing and analysis, rather than simulate the reality. However, mistakes always exist in all less explored concepts and developments. Here, in order to offer a better instruction of deep learning for materials scientists, several recent works on deep learning based material research were reviewed. The application of deep learning in material research is introduced and discussed. As an example of both general material and advanced material, medical material was discussed for the impact and future of deep learning methods. At last, we provided several insights on future work for both deep learning scientists, data scientists and materials scientists.

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Gp-Gan

Cited by 163 publications

References 18 publications

From shallow feature learning to deep learning: Benefits from the width and depth of deep architectures

From shallow feature learning to deep learning: Benefits from the width and depth of deep architectures

College students’ perceptions of AI-based writing learning tools: With a focus on Google Translate, Naver Papago, and Grammarly

Deep Learning for Medical Materials: Review and Perspective

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