A survey and taxonomy of adversarial neural networks for text‐to‐image synthesis

Agnese, Jorge; Herrera, Jonathan; Tao, Haicheng; Zhu, Xingquan

doi:10.1002/widm.1345

Cited by 62 publications

(36 citation statements)

References 63 publications

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“…We can regroup these methods into four categories, as shown in Table 1. Each category has its own characterization [36].  Semantic enhancement: This category represents the GANs methods which focus on generating images semantically related to the input texts, by using the neural network to encode texts as dense features, and fed-it to the second network to generate images matching to the text's description.…”

Section: Methods Categorization Of Gan-based Text-to-image Synthesismentioning

confidence: 99%

Optimal text-to-image synthesis model for generating portrait images using generative adversarial network techniques

Berrahal¹,

Azizi²

2022

IJEECS

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The advancements in artificial intelligence research, particularly in computer vision, have led to the development of previously unimaginable applications, such as generating new contents based on text description. In our work we focused on the text-to-image synthesis applications (TIS) field, to transform descriptive sentences into a real image. To tackle this issue, we use unsupervised deep learning networks that can generate high quality images from text descriptions, provided by eyewitnesses to assist law enforcement in their investigations, for the purpose of generating probable human faces. We analyzed a number of existing approaches and chose the best one. Deep fusion generative adversarial networks (DF-GAN) is the network that performs better than its peers, at multiple levels, like the generated image quality or the respect of the giving descriptive text. Our model is trained on the CelebA dataset and text descriptions (generated by our algorithm using existing attributes in the dataset). The obtained results from our implementation show that the learned generative model makes excellent quantitative and visual performances, the model is capable of generating realistic and diverse samples for human faces and create a complete portrait with respect of given text description.

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Section: Methods Categorization Of Gan-based Text-to-image Synthesismentioning

confidence: 99%

Optimal text-to-image synthesis model for generating portrait images using generative adversarial network techniques

Berrahal¹,

Azizi²

2022

IJEECS

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“…Since the original GANs frameworks were initially built upon images, there is no doubt that the number of GANs applications in the image domain surpasses other areas such as text, voice, and video. Multiple reviews [30][31][32][33][34] focus on image synthesis, even though the major proportion of the general GANs reviews mentioned above, are also in the image domain. Huang et al [31] categorize the image synthesis GANs frameworks into three types based on the overall architecture.…”

Section: Image Gansmentioning

confidence: 99%

“…Similar applications are also reviewed elsewhere [30], with additional applications such as face ageing and 3D image synthesis. Moreover, Agnese et al [34] direct attention to GANs models that are conditioned on text and produce images. Some of these models fall under semantic enhancement GANs, where the main goal is to ensure that text is semantically coherent with the generated image.…”

Section: Image Gansmentioning

confidence: 99%

Video Generative Adversarial Networks: A Review

et al. 2022

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With the increasing interest in the content creation field in multiple sectors such as media, education, and entertainment, there is an increased trend in the papers that use AI algorithms to generate content such as images, videos, audio, and text. Generative Adversarial Networks (GANs) is one of the promising models that synthesizes data samples that are similar to real data samples. While the variations of GANs models in general have been covered to some extent in several survey papers, to the best of our knowledge, this is the first paper that reviews the state-of-the-art video GANs models. This paper first categorizes GANs review papers into general GANs review papers, image GANs review papers, and special field GANs review papers such as anomaly detection, medical imaging, or cybersecurity. The paper then summarizes the main improvements in GANs that are not necessarily applied in the video domain in the first run but have been adopted in multiple video GANs variations. Then, a comprehensive review of video GANs models are provided under two main divisions based on existence of a condition. The conditional models are then further classified according to the provided condition into audio, text, video, and image. The paper concludes with the main challenges and limitations of the current video GANs models.

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“…Researches such as [35] and [36] focus on reviewing recent techniques to incorporate GANs in the problem of text-toimage synthesis. In [35], Agnese et al propose a taxonomy to summarize GAN based text-to-image synthesis papers into four major categories: Semantic Enhancement GANs, Resolution Enhancement GANs, Diversity Enhancement GANS, and Motion Enhancement GANs. Different from the other surveys in this field, Sampath et al [37] examine the most recent developments of GANs techniques for addressing imbalance problems in image data.…”

Section: Gan Applicationsmentioning

confidence: 99%

Game of GANs: Game-Theoretical Models for Generative Adversarial Networks

Moghadam¹,

Boroomand²,

Barzegar-Jalali³

et al. 2021

Preprint

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Generative Adversarial Network, as a promising research direction in the AI community, recently attracts considerable attention due to its ability to generating high-quality realistic data. GANs are a competing game between two neural networks trained in an adversarial manner to reach a Nash equilibrium. Despite the improvement accomplished in GANs in the last years, there remain several issues to solve. In this way, how to tackle these issues and make advances leads to rising research interests. This paper reviews literature that leverages the game theory in GANs and addresses how game models can relieve specific generative models' challenges and improve the GAN's performance. In particular, we firstly review some preliminaries, including the basic GAN model and some game theory backgrounds. After that, we present our taxonomy to summarize the state-of-the-art solutions into three significant categories: modified game model, modified architecture, and modified learning method. The classification is based on the modifications made in the basic model by the proposed approaches from the game-theoretic perspective. We further classify each category into several subcategories. Following the proposed taxonomy, we explore the main objective of each class and review the recent work in each group. Finally, we discuss the remaining challenges in this field and present the potential future research topics.

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A survey and taxonomy of adversarial neural networks for text‐to‐image synthesis

Cited by 62 publications

References 63 publications

Optimal text-to-image synthesis model for generating portrait images using generative adversarial network techniques

Optimal text-to-image synthesis model for generating portrait images using generative adversarial network techniques

Video Generative Adversarial Networks: A Review

Game of GANs: Game-Theoretical Models for Generative Adversarial Networks

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