The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-Tailed Classification
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
(2017) · Cited by 11,625
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 2×
“Similar to human learning, designing an organized curriculum has proved to be effective in the context of machine learning as well [1].”
Background
Cited by
A Survey on Curriculum Learning
Wang, X., Chen, Y., & Zhu, W. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3069908 · Cited by 692
Improving Calibration for Long-Tailed Recognition
Zhong, Z., Cui, J., Liu, S., & Jia, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01622 · Cited by 282
Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection
Wan, Q., Gao, L., & Li, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3205674 · Cited by 22
Improving the Intra-class Long-Tail in 3D Detection via Rare Example Mining
Jiang, C. M., Najibi, M., Qi, C. R., Zhou, Y., & Anguelov, D. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20080-9_10 · Cited by 21
Wang, Y., Cai, Y., Chen, M., Liang, Y., & Hooi, B. (2023) · arXiv · doi:10.48550/arxiv.2310.13206 · Cited by 13
Metadata
- Authors
- Xiang, L., Ding, G., & Han, J.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 247-263
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3096121526
- Cited by
- 268
- References
- 46
- FWCI
- 12.41
- Type
- conference paper
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-Tailed Classification. https://app.cochat.ai/papers/10.1007/978-3-030-58558-7_15
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- Xiang, L., Ding, G., & Han, J. (2020). Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-Tailed Classification. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-58558-7_15
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
