Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies (2012.13736v1)

Abstract: Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, transforming an object or person to a desired shape become a well-studied research in the GANs. GANs are powerful models for learning complex distributions to synthesize semantically meaningful samples. However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training. Given the current fast GANs development, in this survey, we provide a comprehensive review of adversarial models for image synthesis. We summarize the synthetic image generation methods, and discuss the categories including image-to-image translation, fusion image generation, label-to-image mapping, and text-to-image translation. We organize the literature based on their base models, developed ideas related to architectures, constraints, loss functions, evaluation metrics, and training datasets. We present milestones of adversarial models, review an extensive selection of previous works in various categories, and present insights on the development route from the model-based to data-driven methods. Further, we highlight a range of potential future research directions. One of the unique features of this review is that all software implementations of these GAN methods and datasets have been collected and made available in one place at https://github.com/pshams55/GAN-Case-Study.

Overview of "Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies"

The paper "Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies" by Pourya Shamsolmoali et al. provides a thorough examination and assessment of Generative Adversarial Networks (GANs) for synthetic image generation. The paper underscores GANs' significant impact in various domains, including computer vision, medicine, and natural language processing. GANs have emerged as powerful models capable of learning complex distributions to synthesize semantically meaningful samples. Despite their applicability, a need exists for a comprehensive analysis of GAN architectures, loss variants, evaluation metrics, and training stability, which this paper aims to fulfill.

Key Contributions

1. Comprehensive Review of GAN Architectures: The survey organizes the extensive body of literature on GANs for image synthesis based on their architectural innovations, including image-to-image translation, image fusion, label-to-image mapping, and text-to-image translation. The paper delineates the development journey from model-based methods to data-driven methodologies, highlighting seminal works and visionary innovations.
2. Classification of Loss Functions and Evaluation Metrics: The paper presents a taxonomy of loss functions utilized in GANs, addressing the need to mitigate common issues such as mode collapse, vanishing gradients, and unstable convergence. In particular, it identifies alternative loss functions like least square loss and their impact on stable and efficient GAN training.
3. Compilation of Benchmark Datasets: A significant contribution is the compilation and discussion of datasets commonly used to train and evaluate GAN-based image synthesis models. This compilation serves as a critical resource for future research, ensuring consistent benchmarking and evaluation practices across studies.
4. Future Research Directions: The authors spotlight promising avenues for future GAN research, such as improving unsupervised learning capabilities, advancing domain adaptation methods, and handling high-dimensional data challenges. They emphasize integrating GAN research with emerging areas such as video and 3D data synthesis.
5. A Living Repository: A notable aspect of this survey is the provision of a continuously updated online repository that collects software implementations, datasets, and relevant papers. This initiative supports open science practices, enabling easier access and reproducibility of research findings.

Implications of the Research

The research encapsulated in this paper holds both theoretical and practical implications for the evolution of GANs in image synthesis. Theoretically, it encourages a deeper understanding of loss dynamics and architectural choices driving GAN advancements. Practically, the nuanced insights into stable training and implementation strategies inform practitioners on optimizing GAN applications in real-world scenarios, from medical imaging to creative industries like gaming and animation.

Opportunities for Future Developments

As AI and machine learning continue to evolve, future developments in GANs could focus on refining their application to diverse data types and structures beyond standard image datasets. The potential intersection with other deep learning paradigms, such as reinforcement learning, could further enhance GANs' capability in generating more context-aware and feature-rich synthetic data. Moreover, exploring GANs' role in data augmentation strategies now indispensable in training robust machine learning models is a promising research vector.

In conclusion, the paper by Shamsolmoali et al. stands as a pivotal reference for researchers exploring advanced generative models and their application across various domains. Its detailed exploration of GAN-based image synthesis provides valuable insights that can springboard further innovative research efforts in the AI community.