Class Conditional Image Generation

Class-conditional image generation focuses on creating images belonging to specific categories, guided by input labels or conditions. Recent research emphasizes improving the quality and diversity of generated images using various architectures, including diffusion models, autoregressive models, and generative adversarial networks (GANs), often incorporating techniques like efficient fine-tuning strategies and novel sampling methods. These advancements aim to overcome limitations such as mode collapse and computational cost, leading to more robust and scalable image generation for applications ranging from computer vision to drug discovery. The field is actively exploring ways to enhance controllability and diversity while maintaining high fidelity and efficiency.