CF-GAN: cross-domain feature fusion generative adversarial network for text-to-image synthesis

Zhang, Yubo; Han, Shuang; Zhang, Zhongxin; Wang, Jianyang; Bi, Hongbo

doi:10.1007/s00371-022-02404-6

Cited by 15 publications

(4 citation statements)

References 23 publications

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“…Also, MRP-GAN (Qi, Fan, et al, 2021), SAM-GAN (Peng et al, 2021), DM-GAN (M. Zhu, Pan, et al, 2019), DAE-GAN (Ruan et al, 2021), KT-GAN (Tan et al, 2021), Bridge-GAN (M. Yuan & Peng, 2020), CF-GAN (Y. Zhang, Han, et al, 2022), DGattGAN (H. Zhang, Zhu, et al, 2021), PCCM-GAN (Qi, Sun, et al, 2021), aRTIC GAN (Alati et al, 2022), and CDRGAN (M. Wang et al, 2021) were proposed to generate natural images based on a descriptive texts that describe these images. Likewise, Y. Zhou (2021), M. Z.…”

Section: Text-to-image Translationmentioning

confidence: 99%

“…This GAN model takes both image and a text that describes an object to generate a new image containing this object. Also, MRP‐GAN (Qi, Fan, et al, 2021), SAM‐GAN (Peng et al, 2021), DM‐GAN (M. Zhu, Pan, et al, 2019), DAE‐GAN (Ruan et al, 2021), KT‐GAN (Tan et al, 2021), Bridge‐GAN (M. Yuan & Peng, 2020), CF‐GAN (Y. Zhang, Han, et al, 2022), DGattGAN (H. Zhang, Zhu, et al, 2021), PCCM‐GAN (Qi, Sun, et al, 2021), aRTIC GAN (Alati et al, 2022), and CDRGAN (M. Wang et al, 2021) were proposed to generate natural images based on a descriptive texts that describe these images. Likewise, Y. Zhou (2021), M. Z. Khan et al (2021), and Y. Zhou and Shimada (2021) proposed GAN models to synthesize face images based on the text describing these faces.…”

Section: Gan Applicationsmentioning

confidence: 99%

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A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

Mohammed

2023

WIREs Computational Stats

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In machine learning, a generative model is responsible for generating new samples of data in terms of a probabilistic model. Generative adversarial network (GAN) has been widely used to generate realistic samples in different domains and outperforms its peers in the generative models family. However, producing a robust GAN model is not a trivial task because many challenges face the GAN during the training process and impact its performance, affecting the quality and diversity of the generated samples. In this article, we conduct a comprehensive review of GANs to present the fundamentals of GAN, including its components, types, and objective functions. Also, we present an overview of the evaluation matrices used to evaluate GAN models. Moreover, we list the applications of GANs and research work in various domains. Finally, we present the challenges that face GANs and highlight two significant issues, representing mode collapse and training instability, in addition to those research efforts that tackle these challenges.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning Statistical Learning and Exploratory Methods of the Data Sciences > Neural Networks

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Section: Text-to-image Translationmentioning

confidence: 99%

Section: Gan Applicationsmentioning

confidence: 99%

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

Mohammed

2023

WIREs Computational Stats

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“…However, we contend that indirect effects on the environment and the science of ecology are likely going to be of greatest concern (Figure 1). First and foremost, we see risks associated with spreading misinformation via deep fakes (Zhang et al., 2023). Imagine central figures, environmental activists, representatives of environmental NGOs, politicians or ecology experts, for example, being depicted in compromising settings that cast doubt on their integrity, defaming them; thus undermining the validity of the message they send.…”

Section: Introductionmentioning

confidence: 99%

“…However, an emerging trend that is currently unfolding, and that requires in‐depth assessment, is the rise and refinement of text‐to‐image generative models and their multimodal counterparts. These generative models can transform textual prompts into detailed images or videos (Zhang et al., 2023), virtually indistinguishable from actual photos or other original work, including images of environmental content. Such capabilities, accessible without programming expertise, could affect all visual‐related aspects of ecological research and environmental advocacy.…”

Section: Introductionmentioning

confidence: 99%

How widespread use of generative AI for images and video can affect the environment and the science of ecology

Rillig,

Mansour,

Hempel

et al. 2024

Ecology Letters

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Generative artificial intelligence (AI) models will have broad impacts on society including the scientific enterprise; ecology and environmental science will be no exception. Here, we discuss the potential opportunities and risks of advanced generative AI for visual material (images and video) for the science of ecology and the environment itself. There are clearly opportunities for positive impacts, related to improved communication, for example; we also see possibilities for ecological research to benefit from generative AI (e.g., image gap filling, biodiversity surveys, and improved citizen science). However, there are also risks, threatening to undermine the credibility of our science, mostly related to actions of bad actors, for example in terms of spreading fake information or committing fraud. Risks need to be mitigated at the level of government regulatory measures, but we also highlight what can be done right now, including discussing issues with the next generation of ecologists and transforming towards radically open science workflows.

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DrawGAN: Multi-view Generative Model Inspired by the Artist’s Drawing Method

Yang,

Chen,

et al. 2023

Lecture Notes in Computer Science

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CF-GAN: cross-domain feature fusion generative adversarial network for text-to-image synthesis

Cited by 15 publications

References 23 publications

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

How widespread use of generative AI for images and video can affect the environment and the science of ecology

DrawGAN: Multi-view Generative Model Inspired by the Artist’s Drawing Method

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