We study generative AI for images, text, and multimodal data, including data augmentation, curriculum learning, active learning, learning support, and validation of generated content.
The research emphasizes both generation quality and how generated outputs can be evaluated and used responsibly in downstream tasks.
Why It Is Difficult
Evaluation of generative models includes subjective components, making it difficult to design stable quantitative metrics.
Quality, diversity, learning stability, semantic validity, bias, and downstream utility often trade off with one another.
Approach
We design multi-perspective evaluation protocols, analyze correlation with human judgment, and introduce curriculum learning, active learning, and verification loops to improve learning efficiency and output reliability.
Evaluation
We combine quantitative indicators such as FID, IS, CLIP-based metrics, and task-specific downstream scores with human evaluation and semantic consistency checks.
Current Questions
- 1Evaluation design that balances image quality and diversity
- 2Active learning and generative support for efficient annotation
- 3Verification of fairness, bias, and semantic validity in generated outputs