MGAN: Training Generative Adversarial Nets with Multiple Generators
T Hoang, T Le, T Tran, QV Nguyen, D Phung
International Conference on Learning Representations (ICLR)
All research directions
Diffusion & Flow Matching
Generative modeling via diffusion, score-based methods, and flow matching for high-fidelity synthesis.
Goals
To advance the theory and practice of diffusion and flow-based generative models for controllable, high-quality data synthesis.
Overview
This direction develops generative models based on diffusion processes and flow matching. Research covers score-based methods, training dynamics, and controllable synthesis for images, text, and multimodal data.
Key topics
- Diffusion and score-based generative models
- Flow matching and continuous normalizing flows
- Controllable and personalized synthesis
- Generative modeling for vision and language
News
Paper ... accepted at ...
Papers in this direction
Dual Discriminator Generative Adversarial Nets
TD Nguyen, T Le, H Vu, D Phung
Advances in Neural Information Processing Systems (NeurIPS)