Lab Seminar Videos
- Gradient Episodic Memory for Continual Learning (Apr. 12 / Presenter: Gyeongho Kim): https://youtu.be/akrCACQQmC0
- Lopez-Paz, David, and Marc'Aurelio Ranzato. "Gradient episodic memory for continual learning." Advances in neural information processing systems 30 (2017).
- Lopez-Paz, David, and Marc'Aurelio Ranzato. "Gradient episodic memory for continual learning." Advances in neural information processing systems 30 (2017).
- DetCo: Unsupervised Contrastive Learning for Object Detection (Apr. 05 / Presenter: Soyeon Park): https://youtu.be/hWjhy6b01n4
- Xie, Enze, Jian Ding, Wenhai Wang, Xiaohang Zhan, Hang Xu, Peize Sun, Zhenguo Li, and Ping Luo. "Detco: Unsupervised contrastive learning for object detection." In Proceedings of the IEEE/CVF international conference on computer vision, pp. 8392-8401. 2021.
- Xie, Enze, Jian Ding, Wenhai Wang, Xiaohang Zhan, Hang Xu, Peize Sun, Zhenguo Li, and Ping Luo. "Detco: Unsupervised contrastive learning for object detection." In Proceedings of the IEEE/CVF international conference on computer vision, pp. 8392-8401. 2021.
- Diversify: A General Framework for Time Series Out-of-distribution Detection and Generalization (Mar. 22 / Presenter: Sujin Jeon): https://youtu.be/2BLL7r_Hjog
- Lu, Wang, Jindong Wang, Xinwei Sun, Yiqiang Chen, Xiangyang Ji, Qiang Yang, and Xing Xie. "Diversify: A General Framework for Time Series Out-of-distribution Detection and Generalization." IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).
- Lu, Wang, Jindong Wang, Xinwei Sun, Yiqiang Chen, Xiangyang Ji, Qiang Yang, and Xing Xie. "Diversify: A General Framework for Time Series Out-of-distribution Detection and Generalization." IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).
- A Simple Baseline for Bayesian Uncertainty in Deep Learning (Feb 23, 2024 / Presenter: Gyeongho Kim): https://youtu.be/qc4s_VH5JOY
- Maddox, Wesley J., Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson. "A simple baseline for bayesian uncertainty in deep learning." Advances in neural information processing systems 32 (2019).
- Maddox, Wesley J., Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson. "A simple baseline for bayesian uncertainty in deep learning." Advances in neural information processing systems 32 (2019).
- Context-aware Synthesis and Placement of Object Instances (Feb. 02, 2024 / Presenter: Soyeon Park): https://youtu.be/W2a_NPd3Gn8
- Lee, Donghoon, Sifei Liu, Jinwei Gu, Ming-Yu Liu, Ming-Hsuan Yang, and Jan Kautz. "Context-aware synthesis and placement of object instances." Advances in neural information processing systems 31 (2018).
- Lee, Donghoon, Sifei Liu, Jinwei Gu, Ming-Yu Liu, Ming-Hsuan Yang, and Jan Kautz. "Context-aware synthesis and placement of object instances." Advances in neural information processing systems 31 (2018).
- Improving Out-of-Distribution Robustness via Selective Augmentation (Jan. 26, 2024 / Presenter: Sujin Jeon): https://youtu.be/W1sTyYRkd4M
- Yao, Huaxiu, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, and Chelsea Finn. "Improving out-of-distribution robustness via selective augmentation." In International Conference on Machine Learning, pp. 25407-25437. PMLR, 2022.
- Yao, Huaxiu, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, and Chelsea Finn. "Improving out-of-distribution robustness via selective augmentation." In International Conference on Machine Learning, pp. 25407-25437. PMLR, 2022.
- High-Resolution Image Synthesis With Latent Diffusion Models (Jan. 05, 2024 / Presenter: Jae Gyeong Choi): https://youtu.be/r_IdGxJlCIk
- Rombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. "High-resolution image synthesis with latent diffusion models." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10684-10695. 2022.
- Rombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. "High-resolution image synthesis with latent diffusion models." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10684-10695. 2022.
- Averaging Weights Leads to Wider Optima and Better Generalization (Dec. 22, 2023 / Presenter: Gyeongho Kim): https://youtu.be/pWGXhwrDpHo?si=Gocnbz8jRMB5p6tN
- Izmailov, Pavel, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. "Averaging weights leads to wider optima and better generalization." arXiv preprint arXiv:1803.05407 (2018).
- Izmailov, Pavel, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. "Averaging weights leads to wider optima and better generalization." arXiv preprint arXiv:1803.05407 (2018).
- Rethinking Minimal Sufficient Representation in Contrastive Learning (Dec. 15, 2023 / Presenter: Soyeon Park): https://youtu.be/y8VuqD2vAzc
- Wang, Haoqing, Xun Guo, Zhi-Hong Deng, and Yan Lu. "Rethinking minimal sufficient representation in contrastive learning." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16041-16050. 2022.
- Wang, Haoqing, Xun Guo, Zhi-Hong Deng, and Yan Lu. "Rethinking minimal sufficient representation in contrastive learning." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16041-16050. 2022.
- Domain Generalization by Learning and Removing Domain-specific Features (Dec. 01, 2023 / Presenter: Sujin Jeon): https://youtu.be/-sGFzpQQXTo
- Ding, Yu, Lei Wang, Bin Liang, Shuming Liang, Yang Wang, and Fang Chen. "Domain generalization by learning and removing domain-specific features." Advances in Neural Information Processing Systems 35 (2022): 24226-24239.
- Ding, Yu, Lei Wang, Bin Liang, Shuming Liang, Yang Wang, and Fang Chen. "Domain generalization by learning and removing domain-specific features." Advances in Neural Information Processing Systems 35 (2022): 24226-24239.
- Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds (Nov. 10, 2023 / Presenter: Gyeongho Kim): https://youtu.be/yTa8Ae4HvNM?si=SV99AlJr-jggDH4L
- Ash, Jordan T., Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. "Deep batch active learning by diverse, uncertain gradient lower bounds." arXiv preprint arXiv:1906.03671 (2019).
- Ash, Jordan T., Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. "Deep batch active learning by diverse, uncertain gradient lower bounds." arXiv preprint arXiv:1906.03671 (2019).
- Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion (Oct. 13, 2023 / Presenter: Sujin Jeon): https://youtu.be/lhTADMxHU4w
- Yang, Ling, and Shenda Hong. "Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion." In International Conference on Machine Learning, pp. 25038-25054. PMLR, 2022.
- Yang, Ling, and Shenda Hong. "Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion." In International Conference on Machine Learning, pp. 25038-25054. PMLR, 2022.
- Time-Series Representation Learning via Temporal and Contextual Contrasting (Oct. 10, 2023 / Presenter: Jae Gyeong Choi): https://youtu.be/svaTAOyeiNQ
- Eldele, Emadeldeen, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, and Cuntai Guan. "Time-series representation learning via temporal and contextual contrasting." arXiv preprint arXiv:2106.14112 (2021).
- Eldele, Emadeldeen, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, and Cuntai Guan. "Time-series representation learning via temporal and contextual contrasting." arXiv preprint arXiv:2106.14112 (2021).
- Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views (Sep. 01, 2023 / Presenter: Soyeon Park): https://youtu.be/WZ42hmtkktg
- Zhang, Junbo, and Kaisheng Ma. "Rethinking the augmentation module in contrastive learning: Learning hierarchical augmentation invariance with expanded views." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16650-16659. 2022.
- Zhang, Junbo, and Kaisheng Ma. "Rethinking the augmentation module in contrastive learning: Learning hierarchical augmentation invariance with expanded views." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16650-16659. 2022.
- SWAD: Domain Generalization by Seeking Flat Minima (Aug. 04, 2023 / Presenter: Sujin Jeon): https://youtu.be/I9MLR-78QzU
- Cha, Junbum, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park. "Swad: Domain generalization by seeking flat minima." Advances in Neural Information Processing Systems 34 (2021): 22405-22418.
- Cha, Junbum, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park. "Swad: Domain generalization by seeking flat minima." Advances in Neural Information Processing Systems 34 (2021): 22405-22418.
- Domain Generalization: A Survey (Jul. 28, 2023 / Presenter: Jae Gyeong Choi): https://youtu.be/ueH89TwBJlg
- Zhou, Kaiyang, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy. "Domain generalization: A survey." IEEE Transactions on Pattern Analysis and Machine Intelligence (2022).
- Zhou, Kaiyang, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy. "Domain generalization: A survey." IEEE Transactions on Pattern Analysis and Machine Intelligence (2022).
- Active Learning for Convolutional Neural Networks: A Core-Set Approach (Jul. 21, 2023/ Presenter: Gyeongho Kim): https://youtu.be/SogipbE18D4
- Sener, Ozan, and Silvio Savarese. "Active learning for convolutional neural networks: A core-set approach." arXiv preprint arXiv:1708.00489 (2017).
- Sener, Ozan, and Silvio Savarese. "Active learning for convolutional neural networks: A core-set approach." arXiv preprint arXiv:1708.00489 (2017).
- Pixel-Wise Anomaly Detection in Complex Driving Scenes (Jul. 07, 2023 / Presenter: Soyeon Park): https://youtu.be/IvqRv5DvXTI
- Di Biase, Giancarlo, Hermann Blum, Roland Siegwart, and Cesar Cadena. "Pixel-wise anomaly detection in complex driving scenes." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16918-16927. 2021.
- Di Biase, Giancarlo, Hermann Blum, Roland Siegwart, and Cesar Cadena. "Pixel-wise anomaly detection in complex driving scenes." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16918-16927. 2021.
- PCL: Proxy-Based Contrastive Learning for Domain Generalization (Jun. 30, 2023 / Presenter: Sujin Jeon): https://youtu.be/u6_t4KAWPak
- Yao, Xufeng, Yang Bai, Xinyun Zhang, Yuechen Zhang, Qi Sun, Ran Chen, Ruiyu Li, and Bei Yu. "PCL: Proxy-based Contrastive Learning for Domain Generalization." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7097-7107. 2022.
- Yao, Xufeng, Yang Bai, Xinyun Zhang, Yuechen Zhang, Qi Sun, Ran Chen, Ruiyu Li, and Bei Yu. "PCL: Proxy-based Contrastive Learning for Domain Generalization." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7097-7107. 2022.
- Learning Loss for Active Learning (Jun. 22, 2023 / Presenter: Gyeongho Kim): https://youtu.be/lwOysX5pxH0
- Yoo, Donggeun, and In So Kweon. "Learning loss for active learning." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 93-102. 2019.
- Yoo, Donggeun, and In So Kweon. "Learning loss for active learning." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 93-102. 2019.
- Understanding the Behaviour of Contrastive Loss (Apr. 14, 2023 / Presenter: Sujin Jeon): https://youtu.be/AGvwLTXYH7o
- Wang, Feng, and Huaping Liu. "Understanding the behaviour of contrastive loss." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2495-2504. 2021.
- Wang, Feng, and Huaping Liu. "Understanding the behaviour of contrastive loss." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2495-2504. 2021.
- Sharpness-aware Minimization for Efficiently Improving Generalization (Mar. 17, 2023 / Presenter: Gyeongho Kim): https://youtu.be/lcNjbOHf0uo
- Foret, Pierre, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. "Sharpness-aware minimization for efficiently improving generalization." arXiv preprint arXiv:2010.01412 (2020).
- Foret, Pierre, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. "Sharpness-aware minimization for efficiently improving generalization." arXiv preprint arXiv:2010.01412 (2020).
- [Asap-net] Spatially-adaptive pixelwise networks for fast image translation (Feb. 24, 2023 / Presenter: Jae Gyeong Choi): https://youtu.be/drG_YNQ1MVM
- Shaham, Tamar Rott, Michaël Gharbi, Richard Zhang, Eli Shechtman, and Tomer Michaeli. "Spatially-adaptive pixelwise networks for fast image translation." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14882-14891. 2021.
- Shaham, Tamar Rott, Michaël Gharbi, Richard Zhang, Eli Shechtman, and Tomer Michaeli. "Spatially-adaptive pixelwise networks for fast image translation." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14882-14891. 2021.
- A Simple Framework for Contrastive Learning of Visual Representations (Feb. 17, 2023 / Presenter: Sujin Jeon): https://youtu.be/zVaXSKvUncE
- Chen, Ting, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. "A simple framework for contrastive learning of visual representations." In International conference on machine learning, pp. 1597-1607. PMLR, 2020.
- Chen, Ting, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. "A simple framework for contrastive learning of visual representations." In International conference on machine learning, pp. 1597-1607. PMLR, 2020.
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? (Feb. 03, 2023 / Presenter: Gyeongho Kim): https://youtu.be/2XniGoni_5Q
- Kendall, Alex, and Yarin Gal. "What uncertainties do we need in bayesian deep learning for computer vision?." Advances in neural information processing systems 30 (2017).
- Kendall, Alex, and Yarin Gal. "What uncertainties do we need in bayesian deep learning for computer vision?." Advances in neural information processing systems 30 (2017).
- TabNet: Attentive Interpretable Tabular Learning (Jan. 25, 2023 / Presenter: Jae Gyeong Choi): https://youtu.be/uqaEHcOUCIA
- Arik, Sercan Ö., and Tomas Pfister. "TabNet: Attentive Interpretable Tabular Learning." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 8, pp. 6679-6687. 2021.
- Arik, Sercan Ö., and Tomas Pfister. "TabNet: Attentive Interpretable Tabular Learning." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 8, pp. 6679-6687. 2021.
- Tabular data: Deep learning is not all you need (Jan. 18, 2023 / Presenter: Soyeon Park): https://youtu.be/WH39KDQbrb4
- Shwartz-Ziv, Ravid, and Amitai Armon. "Tabular data: Deep learning is not all you need." Information Fusion 81 (2022): 84-90.
- Shwartz-Ziv, Ravid, and Amitai Armon. "Tabular data: Deep learning is not all you need." Information Fusion 81 (2022): 84-90.
- Prototypical Networks for Few-shot Learning (Nov. 18, 2022 / Presenter: Sujin Jeon): https://youtu.be/ogeBqcSYwgQ
- Snell, Jake, Kevin Swersky, and Richard Zemel. "Prototypical networks for few-shot learning." Advances in neural information processing systems 30 (2017).
- Snell, Jake, Kevin Swersky, and Richard Zemel. "Prototypical networks for few-shot learning." Advances in neural information processing systems 30 (2017).
- Weight Uncertainty in Neural Network (Jul. 27, 2022 / Presenter: Gyeongho Kim): https://youtu.be/Pxi_Df6Lx0M
- Blundell, Charles, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. "Weight uncertainty in neural network." In International conference on machine learning, pp. 1613-1622. PMLR, 2015.
- Blundell, Charles, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. "Weight uncertainty in neural network." In International conference on machine learning, pp. 1613-1622. PMLR, 2015.
- Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav) (Jul. 13, 2022 / Presenter: Jae Gyeong Choi): https://youtu.be/oHjQCOHhWMM
- Kim, Been, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, and Fernanda Viegas. "Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)." In International conference on machine learning, pp. 2668-2677. PMLR, 2018.
- Kim, Been, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, and Fernanda Viegas. "Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)." In International conference on machine learning, pp. 2668-2677. PMLR, 2018.
- Domain-adversarial training of neural networks (DANN) (Jul. 01, 2022 / Presenter: Sujin Jeon): https://youtu.be/y1zKpl-rF4E
- Ganin, Yaroslav, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. "Domain-adversarial training of neural networks." The journal of machine learning research17, no. 1 (2016): 2096-2030.
- Ganin, Yaroslav, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. "Domain-adversarial training of neural networks." The journal of machine learning research17, no. 1 (2016): 2096-2030.
- An Explainable Convolutional Neural Network for Fault Diagnosis in Linear Motion Guide (Jan. 13, 2022 / Presenter: Jae Gyeong Choi): https://youtu.be/y71Qb5buby8
- Kim, Min Su, Jong Pil Yun, and PooGyeon Park. "An explainable convolutional neural network for fault diagnosis in linear motion guide." IEEE Transactions on Industrial Informatics 17, no. 6 (2020): 4036-4045.
- Kim, Min Su, Jong Pil Yun, and PooGyeon Park. "An explainable convolutional neural network for fault diagnosis in linear motion guide." IEEE Transactions on Industrial Informatics 17, no. 6 (2020): 4036-4045.
- Big Self-Supervised Models are Strong Semi-Supervised Learners (Nov. 19, 2021 / Presenter: Gyeongho Kim): https://youtu.be/raYCPNskIEE
- Chen, Ting, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E. Hinton. "Big self-supervised models are strong semi-supervised learners." Advances in neural information processing systems 33 (2020): 22243-22255.
- Chen, Ting, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E. Hinton. "Big self-supervised models are strong semi-supervised learners." Advances in neural information processing systems 33 (2020): 22243-22255.
- A novel method for predicting delamination of carbon fiber reinforced plastic (CFRP) based on multi-sensor data (Nov. 05, 2021 / Presenter: Jae Gyeong Choi): https://youtu.be/Xo_8z0XV5nQ
- Cui, Jiacheng, Wei Liu, Yang Zhang, Changyong Gao, Zhe Lu, Ming Li, and Fuji Wang. "A novel method for predicting delamination of carbon fiber reinforced plastic (CFRP) based on multi-sensor data." Mechanical Systems and Signal Processing 157 (2021): 107708.
- Cui, Jiacheng, Wei Liu, Yang Zhang, Changyong Gao, Zhe Lu, Ming Li, and Fuji Wang. "A novel method for predicting delamination of carbon fiber reinforced plastic (CFRP) based on multi-sensor data." Mechanical Systems and Signal Processing 157 (2021): 107708.
- BAM: Bottleneck Attention Module (Sep. 24, 2021 / Presenter: Jae Gyeong Choi): https://youtu.be/KJATJc2EpLc
- Park, Jongchan, Sanghyun Woo, Joon-Young Lee, and In So Kweon. "Bam: Bottleneck attention module." arXiv preprint arXiv:1807.06514 (2018).
- Park, Jongchan, Sanghyun Woo, Joon-Young Lee, and In So Kweon. "Bam: Bottleneck attention module." arXiv preprint arXiv:1807.06514 (2018).
- Stacked Hourglass Networks for Human Pose Estimation (Jun. 04, 2021 / Presenter: Jae Gyeong Choi): https://youtu.be/qxXVv3F3bMk
- Newell, Alejandro, Kaiyu Yang, and Jia Deng. "Stacked hourglass networks for human pose estimation." In European conference on computer vision, pp. 483-499. Springer, Cham, 2016.
- Newell, Alejandro, Kaiyu Yang, and Jia Deng. "Stacked hourglass networks for human pose estimation." In European conference on computer vision, pp. 483-499. Springer, Cham, 2016.
- Attention Is All You Need (Feb. 10, 2021 / Presenter: Gyeongho Kim): https://youtu.be/EOTNLT7vTn8
- Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. "Attention is all you need." Advances in neural information processing systems 30 (2017).
- Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. "Attention is all you need." Advances in neural information processing systems 30 (2017).
