Publications

[C]onference, [J]ournal, [P]reprint, [W]orkshop, *Equal contribution

(NEW! ) [C16] On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning

Jeongheon Oh, Kibok Lee

In ICML, 2024. [coming soon]

(NEW! ) [C15] Learning to Embed Time Series Patches Independently

Seunghan Lee, Taeyoung Park, Kibok Lee

In ICLR, 2024. [paper, arXiv:2312.16427][GitHub]

Preliminary version was presented in NeurIPS Workshop on Self-Supervised Learning: Theory and Practice, 2023. Oral presentation

(NEW! ) [C14] Soft Contrastive Learning for Time Series

Seunghan Lee, Taeyoung Park, Kibok Lee

In ICLR, 2024. Spotlight (366/7262=5%) [paper, arXiv:2312.16424][GitHub]

Preliminary version was presented in NeurIPS Workshop on Self-Supervised Learning: Theory and Practice, 2023.

[P2] ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers

Achin Jain, Kibok Lee, Gurumurthy Swaminathan, Hao Yang, Bernt Schiele, Avinash Ravichandran, Onkar Dabeer

Preprint. [arXiv:2209.05654]

[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]

Preliminary version was presented in ICML Workshop on Self-Supervised Learning for Reasoning and Perception, 2021.

[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]

Preliminary version was presented in NeurIPS Workshop on Self-Supervised Learning: Theory and Practice, 2020.

[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]

Preliminary version [paper][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 (159/3424=4.6%) [paper, arXiv:1901.11300][GitHub]

Preliminary version was presented in NeurIPS Workshop on Bayesian Deep Learning, 2018.

[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]

Preliminary version was presented in ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models, 2018.

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]

Preliminary version was presented in NeurIPS Workshop on Bayesian Deep Learning, 2017.

[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]