(Topics include but are not limited to)
Theoretical and algorithmic innovations of diffusion models
Efficient sampling and acceleration methods for diffusion models
Architecture design of generative models (GAN, VAE, flow models)
Training and optimization of large-scale generative models
Conditional generation and controllable content generation
Multimodal generation (text-image-video-audio)
Application of diffusion models in image generation and editing
Application of diffusion models in video generation and prediction
Application of generative models in 3D content generation
Collaborative learning of generative AI with big data
Quality assessment and credibility of generated data
Interpretability and controllability of generative models
Security alignment and ethical governance of generative models
Content detection and traceability technology
Application of diffusion models in scientific computing
Lightweighting and edge deployment of generative AI models
Federated learning of large models and generative AI
Application of generative AI in data augmentation
Application of generative models in recommendation systems
Application of diffusion models in medical image generation
Copyright and intellectual property protection of generative AI
Data governance and privacy in generative AI training
Application of generative AI in creative industries
Application of diffusion algorithms in time series generation