Research Topics
Publications (C: conference,
J: journal, P: preprint,
*:
equal contribution)
[C13] Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer
In ECCV, 2022. [paper, arXiv:2207.11169][GitHub]
[C12] Improving Transferability of Representations via Augmentation-Aware Self-Supervision
Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, Jinwoo Shin
In NeurIPS, 2021. [paper, arXiv:2111.09613][GitHub]
[C11] i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee
In ICLR, 2021. [paper, arXiv:2010.08887][GitHub][poster]
[P1] ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds
Kibok Lee, Zhuoyuan Chen, Xinchen Yan, Raquel Urtasun, Ersin Yumer
Preprint. [arXiv:2005.11626]
[C10] Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
Kimin Lee*, Kibok Lee*, Jinwoo Shin, Honglak Lee
In ICLR, 2020. [paper, arXiv:1910.05396][GitHub]
Preliminary version [poster] was presented in NeurIPS Workshop on Deep Reinforcement Learning, 2019. Contributed talk
[C9] Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild
Kibok Lee, Kimin Lee, Jinwoo Shin, Honglak Lee
In ICCV, 2019. [paper, arXiv:1903.12648][GitHub][poster]
[Short version][slides] was presented in CVPR Workshop on Uncertainty and Robustness in Deep Visual Learning, 2019. Spotlight
[C8] Robust Inference via Generative Classifiers for Handling Noisy Labels
Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin
In ICML, 2019. Long presentation [paper, arXiv:1901.11300][GitHub]
[C7] Automatic Correction of Lithography Hotspots with a Deep Generative Model
Woojoo Sim*, Kibok Lee*, Dingdong Yang, Jaeseung Jeong, Ji-Suk Hong, Sooryong Lee, Honglak Lee
In SPIE Advanced Lithography, 2019. Invited (long presentation) [paper]
[C6] A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin
In NeurIPS, 2018. Spotlight (168/4856=3.5%) [paper, arXiv:1807.03888][GitHub]
Comment: Adversarial samples can be used to validate our proposed method without OOD samples; check the right side of the Table 2.
[C5] Hierarchical Novelty Detection for Visual Object Recognition
Kibok Lee, Kimin Lee, Kyle Min, Yuting Zhang, Jinwoo Shin, Honglak Lee
In CVPR, 2018. [paper, arXiv:1804.00722][GitHub][poster]
Comment: Hierarchical novelty detection is a generalization of generalized zero-shot learning, in the sense that it does not require semantic information about zero-shot classes.
[C4] Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin
In ICLR, 2018. [paper, arXiv:1711.09325][GitHub]
[C3] Towards Understanding the Invertibility of Convolutional Neural Networks
Anna Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, Honglak Lee
In IJCAI, 2017. [paper, arXiv:1705.08664][slides][poster]
[C2] Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification
Yuting Zhang, Kibok Lee, Honglak Lee
In ICML, 2016. [paper, arXiv:1606.06582][GitHub][slides][poster]
[J1] A Flexible Framework for Online Document Segmentation by Pair-wise Stroke Distance Learning
Adrien Delaye, Kibok Lee
Pattern Recognition, 2015. [paper (ScienceDirect) (ResearchGate)]
[C1] On the Equivalence of Linear Discriminant Analysis and Least Squares
Kibok Lee, Junmo Kim
In AAAI, 2015. [paper (gdrive)] [supplementary]
2022-Present: Assistant Professor, Applied Statistics / Statistics and Data Science at Yonsei University, Seoul, Korea
2020-2022: Applied Scientist, Amazon Web Services, WA, US
2019-2019: Research Intern, Uber Advanced Technologies Group, CA, US
2012-2015: Research Engineer, Samsung Electronics, Suwon, Korea
Ph.D. in Computer Science and Engineering, University of Michigan, 2020
M.S. in Electrical Engineering, KAIST, 2012
B.S. in Electrical Engineering, KAIST, 2011
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Journal of Machine Learning Research (JMLR)
Transactions on Machine Learning Research (TMLR)
International Journal of Computer Vision (IJCV)
AAAI 2020-2022
ACCV 2020-2022
CVPR 2020-2022
ECCV 2020-2022
ICCV 2019-2021
ICLR 2020–2022
ICML 2020–2022
NeurIPS 2019–2022
WACV 2021–2023
Workshop Program Committee
ECCV 2022 Workshop on a Challenge for Out-of-Distribution Generalization in Computer Vision
ECCV 2022 Workshop on Adversarial Robustness in the Real World
NeurIPS 2021 Workshop on Distribution Shifts
ICCV 2021 Workshop on Adversarial Robustness In the Real World
ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning
ECCV 2020 Workshop on Adversarial Robustness in the Real World
ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning
NeurIPS 2018 Workshop on Continual Learning
