5 papers
Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models
Yeongmin Kim, Donghyeok Shin, Byeonghu Na +3
Diffusion models have demonstrated strong generative performance; however, generated samples often fail to fully align with human intent. This paper studies an efficient test-time…
Distillation of Large Language Models via Concrete Score Matching
Yeongmin Kim, Donghyeok Shin, Mina Kang +2
Large language models (LLMs) deliver remarkable performance but are costly to deploy, motivating knowledge distillation (KD) for efficient inference. Existing KD objectives typical…
AMiD: Knowledge Distillation for LLMs with -mixture Assistant Distribution
Donghyeok Shin, Yeongmin Kim, Suhyeon Jo +2
Autoregressive large language models (LLMs) have achieved remarkable improvement across many tasks but incur high computational and memory costs. Knowledge distillation (KD) mitiga…
Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models
Byeonghu Na, Minsang Park, Gyuwon Sim +6
Text-to-image diffusion models rely on text embeddings from a pre-trained text encoder, but these embeddings remain fixed across all diffusion timesteps, limiting their adaptabilit…
Distilling Dataset into Neural Field
Donghyeok Shin, HeeSun Bae, Gyuwon Sim +2
Utilizing a large-scale dataset is essential for training high-performance deep learning models, but it also comes with substantial computation and storage costs. To overcome these…