Implementation of DCGAN to Generate Gamocha Design Patterns

Pradhan, Adarsh; Buragohain, Arunav; Pathak, Uddipta; Ansari, Sahil; Baruah, Manali

doi:10.1007/978-981-15-7031-5_79

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…A growing number of modern image synthesis with a focus on artworks are based on Radford et al's Deep Convolutional Generative Adversarial Network (DCGAN) [10]. Although the research remains unpublished as a preprint, it has retrieved over ten thousand citations at the time of writing with published applications including the generation of artworks [11][12][13], text-to-image synthesis [14,15], converting greyscale images by colourisation [16,17], and data augmentation [18,19]. In their 2017 paper, Tan et al [20] suggested an extension to DCGAN similar to that of the CGAN, noting that backpropagation regarding categorical labels could improve the generation of artworks.…”

Section: Introductionmentioning

confidence: 99%

Continuation of Famous Art with AI: A Conditional Adversarial Network Inpainting Approach

Bird¹

2021

Preprint

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Much of the state-of-the-art in image synthesis inspired by real artwork are either entirely generative by filtered random noise or inspired by the transfer of style. This work explores the application of image inpainting to continue famous artworks and produce generative art with a Conditional GAN. During the training stage of the process, the borders of images are cropped, leaving only the centre. An inpainting GAN is then tasked with learning to reconstruct the original image from the centre crop by way of minimising both adversarial and absolute difference losses, which are analysed by both their Fréchet Inception Distances and manual observations which are presented. Once the network is trained, images are then resized rather than cropped and presented as input to the generator. Following the learning process, the generator then creates new images by continuing from the edges of the original piece. Three experiments are performed with datasets of 4766 landscape paintings (impressionism and romanticism), 1167 Ukiyo-e works from the Japanese Edo period, and 4968 abstract artworks. Results show that geometry and texture (including canvas and paint) as well as scenery such as sky, clouds, water, land (including hills and mountains), grass, and flowers are implemented by the generator when extending real artworks. In the Ukiyo-e experiments, it was observed that features such as written text were generated even in cases where the original image did not have any, due to the presence of an unpainted border within the input image.

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Section: Introductionmentioning

confidence: 99%

Continuation of Famous Art with AI: A Conditional Adversarial Network Inpainting Approach

Bird¹

2021

Preprint

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A Transfer Deep Generative Adversarial Network Model to Synthetic Brain CT Generation from MR Images

Zheng

2021

Wireless Communications and Mobile Computing

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Background. The generation of medical images is to convert the existing medical images into one or more required medical images to reduce the time required for sample diagnosis and the radiation to the human body from multiple medical images taken. Therefore, the research on the generation of medical images has important clinical significance. At present, there are many methods in this field. For example, in the image generation process based on the fuzzy C-means (FCM) clustering method, due to the unique clustering idea of FCM, the images generated by this method are uncertain of the attribution of certain organizations. This will cause the details of the image to be unclear, and the resulting image quality is not high. With the development of the generative adversarial network (GAN) model, many improved methods based on the deep GAN model were born. Pix2Pix is a GAN model based on UNet. The core idea of this method is to use paired two types of medical images for deep neural network fitting, thereby generating high-quality images. The disadvantage is that the requirements for data are very strict, and the two types of medical images must be paired one by one. DualGAN model is a network model based on transfer learning. The model cuts the 3D image into multiple 2D slices, simulates each slice, and merges the generated results. The disadvantage is that every time an image is generated, bar-shaped “shadows” will be generated in the three-dimensional image. Method/Material. To solve the above problems and ensure the quality of image generation, this paper proposes a Dual3D&PatchGAN model based on transfer learning. Since Dual3D&PatchGAN is set based on transfer learning, there is no need for one-to-one paired data sets, only two types of medical image data sets are needed, which has important practical significance for applications. This model can eliminate the bar-shaped “shadows” produced by DualGAN’s generated images and can also perform two-way conversion of the two types of images. Results. From the multiple evaluation indicators of the experimental results, it can be analyzed that Dual3D&PatchGAN is more suitable for the generation of medical images than other models, and its generation effect is better.

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Implementation of DCGAN to Generate Gamocha Design Patterns

Cited by 2 publications

References 3 publications

Continuation of Famous Art with AI: A Conditional Adversarial Network Inpainting Approach

Continuation of Famous Art with AI: A Conditional Adversarial Network Inpainting Approach

A Transfer Deep Generative Adversarial Network Model to Synthetic Brain CT Generation from MR Images

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