Project Background:
The Style-Based Generative Adversarial Networks (GANs) revolve around advancing state-of-the-art generative modeling in image synthesis. Traditional GANs have shown remarkable success in generating high-quality images but often struggle with controlling specific attributes or styles of the generated images. Style-based GANs address this limitation by introducing disentangled representations of content and style, enabling finer control over various aspects of image generation. This disentanglement separates high-level semantic content from low-level stylistic attributes, leading to more flexible and controllable image synthesis. This project aims to explore and refine the techniques to push the boundaries of image synthesis, ranging from artistic creation and entertainment to data augmentation in various domains such as fashion, design, and entertainment. Ultimately, the goal is to develop Style-Based GANs that can produce highly realistic and customizable images with unprecedented levels of control and fidelity.
Problem Statement
- Traditional GANs struggle with controlling specific attributes or styles of generated images limiting their applicability in tasks requiring fine-grained control.
- Need for techniques that can disentangle high-level semantic content from low-level stylistic attributes to enable more flexible and controllable image synthesis.
- Enhancing the realism and diversity of generated images remains challenging in scenarios where precise control over attributes is desired.
- Scaling up to handle high-resolution images while maintaining efficiency and stability poses a significant challenge in large-scale image synthesis tasks.
Aim and Objectives
- To advance generative modeling capabilities for high-quality and controllable image synthesis using Style-Based Generative Adversarial Networks.
- Develop techniques for disentangling high-level semantic content from low-level stylistic attributes in generated images.
- Enhance the realism, diversity, and controllability of synthesized images through improved style-based synthesis methods.
- Explore strategies for scaling up Style-Based GANs to handle high-resolution images efficiently and effectively.
- Investigate applications of Style-Based GANs in various domains, including art generation, fashion design, and data augmentation.
- Validate the effectiveness through quantitative and qualitative evaluation metrics, demonstrating utility and performance compared to existing methods.
Contributions to Style-Based Generative Adversarial Networks
- Developed techniques for disentangling high-level semantic content from low-level stylistic attributes in generated images, enabling finer control over image synthesis.
- Improved the realism, diversity, and controllability of synthesized images through advancements in style-based synthesis methods.
- Explored strategies for scaling up Style-Based GANs to handle high-resolution images efficiently, facilitating large-scale image synthesis tasks.
- Validated the effectiveness of the developed approaches through comprehensive evaluation metrics and benchmarks, showcasing their performance compared to existing methods.
Deep Learning Algorithms for Style-Based Generative Adversarial Networks
- StyleGAN
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StyleGAN2
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StyleGAN2-ADA (Adaptive Discriminator Augmentation)
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StyleGAN3
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StyleGAN4
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StyleGAN-NADA (Non-Adversarial Data Augmentation)
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StyleGAN-MMA (Maximum Mean Discrepancy)
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StyleGAN-LADA (Latent Discriminant Adaptation)
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StyleGAN-ADA (Adaptive Data Augmentation)
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StyleGAN-AE (Autoencoder)
Datasets for Style-Based Generative Adversarial Networks
- CelebA
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LSUN (Large-scale Scene Understanding)
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CIFAR-10
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CIFAR-100
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ImageNet
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FFHQ (Flickr-Faces-HQ)
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COCO (Common Objects in Context)
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Places365
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LSUN Bedrooms
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LSUN Cars
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
