Jihoon Tack

I am a Ph.D. student at ALIN-LAB, advised by Prof. Jinwoo Shin at Korea Advanced Institute of Science and Technology (KAIST).

My research focuses on designing algorithms to tackle distributional shifts in machine learning, i.e., training and testing distribution differs. Such research is critical for many real-world applications with trustworthy and safe usage, e.g., autonomous driving and robotics. To this end, I focus on developing (i) robust representation learning schemes under various distributional shifts and (ii) quick adaptation algorithms for unseen distributions and tasks. In addition, I have a broad interest in handling complex signals efficiently by utilizing implicit neural representations.

(C: Conference, W: Workshop, P: Preprint, *: Equal contribution)

[C5/W] Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks

Sihyun Yu*, Jihoon Tack*, Sangwoo Mo*, Hyunsu Kim, Junho Kim, Jung-Woo Ha, Jinwoo Shin
International Conference on Learning Representations (ICLR) 2022
CVPR Workshop on AI for Content Creation (AI4CC) 2022
[paper] [code] [slide] [project page]

[C4/W] Consistency Regularization for Adversarial Robustness

Jihoon Tack, Sihyun Yu, Jongheon Jeong, Minseon Kim, Sung Ju Hwang, Jinwoo Shin
AAAI Conference on Artificial Intelligence (AAAI) 2022
ICML Workshop on Adversarial Machine Learning (AdvML) 2021, Oral presentation
[paper] [code] [slide] [poster]

[C3/W] Meta-Learning Sparse Implicit Neural Representations

Jaeho Lee*, Jihoon Tack*, Namhoon Lee, Jinwoo Shin
Conference on Neural Information Processing Systems (NeurIPS) 2021
Sparsity in Neural Networks workshop (SNN) 2021
[paper] [code] [slide] [poster]

[C2] CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Jihoon Tack*, Sangwoo Mo*, Jongheon Jeong, Jinwoo Shin
Conference on Neural Information Processing Systems (NeurIPS) 2020
Qualcomm Innovation Fellowship Korea 2020
[paper] [code] [slide] [poster]

[C1] Adversarial Self-Supervised Contrastive Learning

Minseon Kim, Jihoon Tack, Sung Ju Hwang
Conference on Neural Information Processing Systems (NeurIPS) 2020
[paper] [code] [project page]

[W2] Consistency Regularization for Training Confidence-Calibrated Classifiers

Youngbum Hur*, Jihoon Tack*, Eunho Yang, Sung Ju Hwang, Jinwoo Shin
ICML Workshop on Uncertainty & Robustness in Deep Learning (UDL) 2021
[paper]

[W1] Entropy Weighted Adversarial Training

Minseon Kim, Jihoon Tack, Jinwoo Shin, Sung Ju Hwang
ICML Workshop on Adversarial Machine Learning (AdvML) 2021
[paper]

• M.S. & Ph.D. integrated course in Artificial Intelligence, Korea Advanced Institute of Science and Technology (KAIST), 2019 - present (advisor: Prof. Jinwoo Shin)
• B.S. in Electrical Engineering and Mathematics (minored), Korea Advanced Institute of Science and Technology (KAIST), 2014 - 2019

• Best Paper Award, Korean Artificial Intelligence Association 2021
• Outstanding Reviewer Award, CVPR 2021
• Winner, Qualcomm Innovation Fellowship Korea 2020
• Top Reviewer Award (top 10%), NeurIPS 2020

• “Consistency Regularization for Adversarial Robustness”
  • @ Korean Artificial Intelligence Association (KAIA), Oral Presentation, Nov 2021
  • @ ICML workshop on AdvML (2021), Contribution Talk, Jul 2021
• “CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances”
  • @ Qualcomm AI Research, Innovation Fellowship Winner Talk, Feb 2021
  • @ NeurIPS Social: ML in Korea, Contribution Talk, Dec 2020
  • @ Korea Software Congress (KSC), Oral Presentation, Dec 2020

• Conference Reviewer
  • NeurIPS 2020, 2021, 2022
  • ICML 2021 (expert), 2022
  • ICLR 2022
  • CVPR 2021, 2022
  • ICCV 2021
• Journal Reviewer
  • IEEE Transactions on Neural Networks and Learning Systems
  • Transactions on Machine Learning Research

• Head TA, AI503: Mathematics for AI, KAIST Fall 2021
• Head TA, AI602: Recent Advances in Deep Learning, KAIST Spring 2021
• TA, AI504: Programming for AI, KAIST Fall 2020
• TA, AI-Expert Program, Samsung-DS Summer 2020
• TA, AI703: Systems and Applications of AI and ML, KAIST Spring 2020
• TA, EE209: Programming Structure for Electrical Engineering, KAIST Fall 2019

• Uncertainty for Interpretable Deep Learning, 2020 - 2022
• Confidence-calibrated Regression for Large-scale Used Car Dataset, 2017 - 2020
  Develop an AI system that predicts price, depreciation, and sales duration of used cars.
  Currently, my system is deployed at KBchachacha service (only Korean supported).