Generative adversarial networks (GANs) have been the most promising AI algorithms in recent years. These are one of the newest fields in machine learning, reaching new heights for their malleable applications in computer vision, data science, or any domain. There are numerous applications for the large-scale, effective production of GANs images, which are used to solve various image-to-image translations. Now, let’s talk about generative adversarial networks (GANs) and the top GANs images produced in image generation projects.
What is Generative Adversarial Network?
A generative Adversarial Network is a generative modeling approach. GAN takes two neural networks to compete in the form of a zero-sum game to give predictions more accurately. These two neural networks are called the generator and the discriminator, which enables an unsupervised learning module. Unsupervised learning is a learning algorithm that learns patterns from untagged data. Similar to mimicry in evolutionary biology, the neural network is expected to learn and find hidden patterns or data groupings in the data. GAN is a class of machine-learning models introduced by Ian Goodfellow and his colleagues in 2014.
The GANs became popular among researchers quickly because of their property to generate new data with the same statistics as the input training set. It can be applied to images, videos, textual data, and more, proving useful for semi-supervised, fully supervised, and reinforcement learning. The base idea of a GAN is to have an indirect training approach via a generator and discriminator. The generator is a convolutional neural network that works to fool the discriminator by artificially producing outputs. And the discriminator is a deconvolutional neural network responsible for telling how realistic the input seems, which gets updated dynamically.
The research with GAN has been at its peak since it was introduced as the innovations of GAN follow great success in computer vision. The most popular GAN architectures are CycleGAN, StyleGAN, pixelRNN, text-2-image, DiscoGAN, and IsGAN. Various real-world applications of GANs deal with images, audio, videos, and text data, including realistic image generation, improving the quality of photographs, audio synthesis, transfer learning, and many more. In recent times, there has been a tremendous success in the production of GANs images, including Deepfake Apps, and with some more fine-tuning, GANs will give state-of-art results.
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Image generation projects using GAN
Image generation and image synthesis are one of the most important aspects of GANs application which can be applied in many fields. The few projects mentioned below are one of the best GANs images produced in the last few years.
Anime Characters
GANs are changing the way of generating realistic anime characters and bringing out the potential of complex GAN architecture to build entire anime series with the help of AI. In the paper “Towards the Automatic Anime Characters Creation with Generative Adversarial Networks,” Yanghua Jin and his team trained and used GAN to generate the faces of anime characters or Japanese comic book characters in 2017. The outcome of the project was remarkable, which led people to conduct more experiments on the image generation of faces of anime characters and the generation of pokemon characters. Many GAN models are used to generate cartoon characters, such as DCGAN, StyleGAN, and so on.
Fake Human Faces
Facial recognition has many use cases, and the development has been in progress for the last couple of years, where researchers are using different techniques and facial recognition datasets to train models. Researchers need massive datasets of human faces to understand the recognition process, and the generation of fake human faces helps these projects. NVIDIA researchers published a paper, “Progressive Growing of GANs for Improved Quality, Stability, and Variation,” in 2018, where they proposed a new training methodology for GANs operating on the generation of feasible human face photographs. The paper’s outcome is so realistic-looking that it can fool anyone easily. Additionally, the paper presented the generation of objects and scenes as well.
Image Style Transfer
Image style transfer is an interesting technique in computer vision that combines two images. This technique consists of a model taking two images, called content and reference images, and the output is a whole new image containing the object of the content image and the style of the reference image. Here by style, it means brush strokes, colors, and textures of the image. Researchers are still trying to find the best ways and use cases of style transfer. This technique is included in the image-to-image translation and is also known as domain adaptation. There are many research work on image style transfer using GANs, and most have produced good results. A remarkable study is the paper “P²-GAN: EFFICIENT STYLE TRANSFER USING SINGLE STYLE IMAGE.” Zhentan Zheng and Jianyi Liu put forth a novel patch permutation GAN (P²-GAN), which proves efficient in learning stroke styles from paintings or single style images. The paper concludes with an effective and precise P²-GAN network simulating the expected stroke style and avoiding the difficulty of collecting image sets with the same style.
Text-to-Image (text-2-image) Synthesis
Generating realistic images is challenging, but using GANs makes the process a reality rather than theories. Although we got problems with image-to-image generation or translation, the synthesis of text-to-image realistic images using GANs is more complex and difficult. The process of synthesis needs a strong GAN structure along with the base images provided. There are many papers on the task which have computed impressive outcomes. In 2016, Han Zhang and his team from the Chinese University of Hong Kong presented “StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks,” explaining the use of GANs. Mainly, the StackGAN to generate realistic photographs from textual data of simple objects such as birds and flowers. Also, the paper “Generative Adversarial Text to Image Synthesis” and “TAC-GAN – Text Conditioned Auxiliary Classifier Generative Adversarial Network” present the study of generating realistic images through text-to-image synthesis with the help of GANs.
Image-to-Image Translation
Image-to-image translation includes many tasks, such as the translation of semantic images to photographs, satellite photographs to google maps, black and white photographs to color, and more. Many papers have been published demonstrating the use of GANs for image-to-image translation, and one of the popular papers is “Image-to-Image Translation with Conditional Adversarial Networks” by Berkeley AI Research group in 2016. This paper has an investigational approach toward conditional adversarial networks if they serve as a general-purpose solution to image-to-image translation problems. They use a unique pix2pix approach for various image-to-image translation tasks. Additionally, the paper titled “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks” presents the famous CycleGAN and a set of impressive image-to-image translation cases like translation from photograph to artistic painting style, photographs from summer to winter, and translation of horse to zebra.
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List of top Generative Adversarial Networks Images
These are the top images made by GANs from research papers for image generation using GANs. The list consists of GANs images produced in the papers mentioned earlier and a few other interesting papers with significant outputs.
1. Cartoon Face Images
The image shown is from the anime character faces generation project in the paper “Towards the Automatic Anime Characters Creation with Generative Adversarial Networks.” The image is generated with the fixed noise part and random attributes. For more information, check out the paper.
2. Forged Face Images
The image shown is made by GAN from the paper “Progressive Growing of GANs for Improved Quality, Stability, and Variation.” The GANs image shown is the images generated using the CelebA dataset with high resolution.
The image shown is made by GAN from the paper “Progressive Growing of GANs for Improved Quality, Stability, and Variation.” The GANs image shown is the images generated using the CelebA dataset with high resolution.
This GANs image is from a famous paper, “A Style-Based Generator Architecture for Generative Adversarial Networks,” which is associated with the generation of the Flicker Faces HQ dataset. The paper studies an alternative generator architecture for GAN with some style transfer literature. The image presented here includes the images synthesized by mixing two latent codes at various scales. And each subset of styles controls meaningful high-level attributes of the image.
This GANs image is from a famous paper, “A Style-Based Generator Architecture for Generative Adversarial Networks,” which is associated with the generation of the Flicker Faces HQ dataset. The paper studies an alternative generator architecture for GAN with some style transfer literature. The image presented here includes the images synthesized by mixing two latent codes at various scales. And each subset of styles controls meaningful high-level attributes of the image.
3. Artificial Flower Images
The text-2-image synthesis is a topic of interest for many researchers. Here the image shown is the GANs image from the paper “Text-to-Image-to-Text Translation using Cycle Consistent Adversarial Networks,” which is a result of GAN trained with cycle loss and frozen weight of image captioning network. Check out the paper for a better understanding.
The text-2-image synthesis is a topic of interest for many researchers. Here the image shown is the GANs image from the paper “Text-to-Image-to-Text Translation using Cycle Consistent Adversarial Networks,” which is a result of GAN trained with cycle loss and frozen weight of image captioning network. Check out the paper for a better understanding.
4. Artificial Bird Images
As mentioned above, the paper “StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks” provides a significant output in text-2-image synthesis. The GANs image here is the image generated by StackGAN, which is very photo-realistic.
5. Drawing Real Objects and Vice-versa
As mentioned in the image-to-image translation, the paper “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks” has one of the best GANs images generated to date. Here, the above image is the result of CycleGAN on paired datasets used in pix2pix.
As mentioned in the image-to-image translation, the paper “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks” has one of the best GANs images generated to date. Here, the above image is the result of CycleGAN on paired datasets used in pix2pix.
6. Fake Paintings
The image shown is from the paper “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks” and is the collection style transfer II, showcasing the successful style transfer into the artistic styles of Monet, Van Gogh, Cezanne, and Ukiyo-e. The paper has also applied methods to solve image-to-image translation problems and can be improved in the future.
The image shown is from the paper “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks” and is the collection style transfer II, showcasing the successful style transfer into the artistic styles of Monet, Van Gogh, Cezanne, and Ukiyo-e. The paper has also applied methods to solve image-to-image translation problems and can be improved in the future.
7. Altering Photographs
This is a GANs image from the paper “P²-GAN: EFFICIENT STYLE TRANSFER USING SINGLE STYLE IMAGE.” It shows a comparative image of the style transfer from a single style image using various methods, including JohnsonNet, TextureNetIN, MGAN, and P²-GAN.
This is a GANs image from the paper “P²-GAN: EFFICIENT STYLE TRANSFER USING SINGLE STYLE IMAGE.” It shows a comparative image of the style transfer from a single style image using various methods, including JohnsonNet, TextureNetIN, MGAN, and P²-GAN.
