The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
SCAN: Learning to Classify Images Without Labels
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Citations
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 classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Representation Learning with Contrastive Predictive Coding
Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691
Cited by
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 3,111
“• Self supervised learning (SSL) [243], [490] is an important subset of unsupervised learning for generating output labels from different data objects by find-”
BackgroundPrototypical Contrastive Learning of Unsupervised Representations
Li, J., Zhou, P., Xiong, C., & Hoi, S. C. H. (2020) · arXiv · doi:10.48550/arxiv.2005.04966 · Cited by 611
A Survey on Semi-, Self- and Unsupervised Learning for Image Classification
Schmarje, L., Santarossa, M., Schroder, S.-M., & Koch, R. (2021) · IEEE Access · doi:10.1109/access.2021.3084358 · Cited by 246
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
Van Gansbeke, W., Vandenhende, S., Georgoulis, S., & Van Gool, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00990 · Cited by 231
Self-citationPiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering
Hyun Cho, J., Mall, U., Bala, K., & Hariharan, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01652 · Cited by 159
Few-Shot Class Incremental Learning Leveraging Self-Supervised Features
Ahmad, T., Dhamija, A. R., Cruz, S., Rabinowitz, R., Li, C., Jafarzadeh, M., & Boult, T. E. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00435 · Cited by 41
Improving Representation Learning for Histopathologic Images with Cluster Constraints
Wu, W., Gao, C., DiPalma, J., Vosoughi, S., & Hassanpour, S. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01957 · Cited by 16
Metadata
- Authors
- Van Gansbeke, W., Vandenhende, S., Georgoulis, S., Proesmans, M., & Van Gool, L.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 268-285
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3039616371
- Cited by
- 550
- References
- 18
- FWCI
- 31.75
- Type
- conference paper
- Language
- en
- Open access
- green
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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.
