On the Importance of Calibration in Semi-supervised Learning

On the Importance of Calibration in Semi-supervised Learning

10 October 2022

Rumen Dangovski

Shivchander Sudalairaj

Leonid Karlinsky

Akash Srivastava

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Papers citing "On the Importance of Calibration in Semi-supervised Learning"

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Title
SpikeMatch: Semi-Supervised Learning with Temporal Dynamics of Spiking Neural Networks Jini Yang Beomseok Oh Seungryong Kim S. Kim 85 0 0 26 Sep 2025
Continuous Contrastive Learning for Long-Tailed Semi-Supervised RecognitionNeural Information Processing Systems (NeurIPS), 2024 Zi-Hao Zhou Siyuan Fang Zi-Jing Zhou Tong Wei Yuanyu Wan Min-Ling Zhang 187 9 0 08 Oct 2024
EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier Logits Mateusz Ochal Massimiliano Patacchiola Malik Boudiaf Sen Wang 177 0 0 04 Aug 2024
Learning Label Refinement and Threshold Adjustment for Imbalanced Semi-Supervised Learning Zeju Li Ying-Qiu Zheng Chen Chen Saâd Jbabdi 133 0 0 07 Jul 2024
Flexible Distribution Alignment: Towards Long-tailed Semi-supervised Learning with Proper CalibrationEuropean Conference on Computer Vision (ECCV), 2023 Emanuel Sanchez Aimar Hannah Helgesen Yonghao Xu Marco Kuhlmann Michael Felsberg 231 3 0 07 Jun 2023
RoPAWS: Robust Semi-supervised Representation Learning from Uncurated DataInternational Conference on Learning Representations (ICLR), 2023 Sangwoo Mo Jong-Chyi Su Chih-Yao Ma Mido Assran Ishan Misra Licheng Yu Sean Bell AI4TS 131 15 0 28 Feb 2023