Research Overview 연구 개요

MLPR Lab researches efficient AI algorithms and models using Machine Learning and Pattern Recognition. Focus areas include model compression via quantization, pruning, and knowledge distillation, as well as machine unlearning and continual learning. The goal is 'Ubiquitous AI' - making AI accessible to everyone, everywhere.

MLPR 연구실은 머신러닝과 패턴인식을 활용하여 효율적인 AI 알고리즘과 모델을 연구합니다. 양자화, 프루닝, 지식 증류를 통한 모델 압축과 머신 언러닝, 지속 학습 등을 주요 연구 분야로 다루고 있습니다.

Research Areas 연구 분야

• Model Compression
• Quantization
• Knowledge Distillation
• Machine Unlearning
• Pruning
• Efficient AI

• 모델 압축
• 양자화
• 지식 증류
• 머신 언러닝
• 프루닝
• 효율적 AI

Major Achievements 주요 연구 성과

• Nonlinear Color Transfer via Learnable Bezier Flows (CVPR 2026)
• Feature-map-level Online Adversarial Knowledge Distillation (ICML 2020)
• Paraphrasing Complex Network: Factor Transfer (NeurIPS 2018)

Honors & Awards 수상

• 2nd Place, Machine Unlearning Challenge (NeurIPS 2023)
• 1st Place, DCASE Challenge Task 1-a
• 3rd Place, MicroNet Challenge (NeurIPS 2019)