13 citations · 24 across the 10 of their papers we have counts for
4 papers · 1 filter
Test-time Alignment of Diffusion Models without Reward Over-optimization
Sunwoo Kim, Minkyu Kim, Dongmin Park
Diffusion models excel in generative tasks, but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often s…
Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance
Dongmin Park, Sebin Kim, Taehong Moon +3
State-of-the-art text-to-image (T2I) diffusion models often struggle to generate rare compositions of concepts, e.g., objects with unusual attributes. In this paper, we show that t…
Discovering and Mitigating Visual Biases through Keyword Explanation
Younghyun Kim, Sangwoo Mo, Minkyu Kim +3
Addressing biases in computer vision models is crucial for real-world AI deployments. However, mitigating visual biases is challenging due to their unexplainable nature, often iden…
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness
Jongheon Jeong, Sejun Park, Minkyu Kim +3
Randomized smoothing is currently a state-of-the-art method to construct a certifiably robust classifier from neural networks against -adversarial perturbations. Under the…