Colour and Brush Stroke Pattern Recognition in Abstract Art using Modified Deep Convolutional Generative Adversarial Networks

• URL: http://arxiv.org/abs/2403.18397v1
• Date: Wed, 27 Mar 2024 09:35:56 GMT
• Title: Colour and Brush Stroke Pattern Recognition in Abstract Art using Modified Deep Convolutional Generative Adversarial Networks
• Authors: Srinitish Srinivasan, Varenya Pathak,
• Abstract summary: This paper describes the study of a wide distribution of abstract paintings using Generative Adrial Neural Networks(GAN) The challenge lies in developing an efficient GAN architecture that overcomes common training pitfalls. This paper introduces a modified-DCGAN (mDCGAN) specifically designed for high-quality artwork generation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Abstract Art is an immensely popular, discussed form of art that often has the ability to depict the emotions of an artist. Many researchers have made attempts to study abstract art in the form of edge detection, brush stroke and emotion recognition algorithms using machine and deep learning. This papers describes the study of a wide distribution of abstract paintings using Generative Adversarial Neural Networks(GAN). GANs have the ability to learn and reproduce a distribution enabling researchers and scientists to effectively explore and study the generated image space. However, the challenge lies in developing an efficient GAN architecture that overcomes common training pitfalls. This paper addresses this challenge by introducing a modified-DCGAN (mDCGAN) specifically designed for high-quality artwork generation. The approach involves a thorough exploration of the modifications made, delving into the intricate workings of DCGANs, optimisation techniques, and regularisation methods aimed at improving stability and realism in art generation enabling effective study of generated patterns. The proposed mDCGAN incorporates meticulous adjustments in layer configurations and architectural choices, offering tailored solutions to the unique demands of art generation while effectively combating issues like mode collapse and gradient vanishing. Further this paper explores the generated latent space by performing random walks to understand vector relationships between brush strokes and colours in the abstract art space and a statistical analysis of unstable outputs after a certain period of GAN training and compare its significant difference. These findings validate the effectiveness of the proposed approach, emphasising its potential to revolutionise the field of digital art generation and digital art ecosystem.

Related papers

• Diffusion-Based Visual Art Creation: A Survey and New Perspectives [51.522935314070416]
  This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives. Our findings reveal how artistic requirements are transformed into technical challenges and highlight the design and application of diffusion-based methods within visual art creation. We aim to shed light on the mechanisms through which AI systems emulate and possibly, enhance human capacities in artistic perception and creativity.
  arXiv  Detail & Related papers  (2024-08-22T04:49:50Z)
• Detecting Generated Images by Real Images Only [64.12501227493765]
  Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace.
  arXiv  Detail & Related papers  (2023-11-02T03:09:37Z)
• Graphical Object-Centric Actor-Critic [55.2480439325792]
  We propose a novel object-centric reinforcement learning algorithm combining actor-critic and model-based approaches. We use a transformer encoder to extract object representations and graph neural networks to approximate the dynamics of an environment. Our algorithm performs better in a visually complex 3D robotic environment and a 2D environment with compositional structure than the state-of-the-art model-free actor-critic algorithm.
  arXiv  Detail & Related papers  (2023-10-26T06:05:12Z)
• RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model [93.8067369210696]
  Text-to-image generation (TTI) refers to the usage of models that could process text input and generate high fidelity images based on text descriptions. Diffusion models are one prominent type of generative model used for the generation of images through the systematic introduction of noises with repeating steps. In the era of large models, scaling up model size and the integration with large language models have further improved the performance of TTI models.
  arXiv  Detail & Related papers  (2023-09-02T03:27:20Z)
• IT3D: Improved Text-to-3D Generation with Explicit View Synthesis [71.68595192524843]
  This study presents a novel strategy that leverages explicitly synthesized multi-view images to address these issues. Our approach involves the utilization of image-to-image pipelines, empowered by LDMs, to generate posed high-quality images. For the incorporated discriminator, the synthesized multi-view images are considered real data, while the renderings of the optimized 3D models function as fake data.
  arXiv  Detail & Related papers  (2023-08-22T14:39:17Z)
• Augmenting Character Designers Creativity Using Generative Adversarial Networks [0.0]
  Generative Adversarial Networks (GANs) continue to attract the attention of researchers in different fields. Most recent GANs are focused on realism, however, generating hyper-realistic output is not a priority for some domains. We present a comparison between different GAN architectures and their performance when trained from scratch on a new visual characters dataset. We also explore alternative techniques, such as transfer learning and data augmentation, to overcome computational resource limitations.
  arXiv  Detail & Related papers  (2023-05-28T10:52:03Z)
• Synergy of Machine and Deep Learning Models for Multi-Painter Recognition [0.0]
  We introduce a new large dataset for painting recognition task including 62 artists achieving good results. RegNet performs better in exporting features, while SVM makes the best classification of images based on the painter with a performance of up to 85%.
  arXiv  Detail & Related papers  (2023-04-28T11:34:53Z)
• Investigating GANsformer: A Replication Study of a State-of-the-Art Image Generation Model [0.0]
  We reproduce and evaluate a novel variation of the original GAN network, the GANformer. Due to resources and time limitations, we had to constrain the network's training times, dataset types, and sizes.
  arXiv  Detail & Related papers  (2023-03-15T12:51:16Z)
• DC-Art-GAN: Stable Procedural Content Generation using DC-GANs for Digital Art [4.9631159466100305]
  We advocate the concept of using deep generative networks with adversarial training for a stable and variant art generation. The work mainly focuses on using the Deep Convolutional Generative Adversarial Network (DC-GAN) and explores the techniques to address the common pitfalls in GAN training.
  arXiv  Detail & Related papers  (2022-09-06T23:06:46Z)
• Dynamically Grown Generative Adversarial Networks [111.43128389995341]
  We propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator.
  arXiv  Detail & Related papers  (2021-06-16T01:25:51Z)
• A deep learning approach to clustering visual arts [7.363576598794859]
  We propose DELIUS: a DEep learning approach to cLustering vIsUal artS. The method uses a pre-trained convolutional network to extract features and then feeds these features into a deep embedded clustering model. The task of mapping the raw input data to a latent space is optimized jointly with the task of finding a set of cluster centroids in this latent space.
  arXiv  Detail & Related papers  (2021-06-11T08:35:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.