Conference item
Invariant information clustering for unsupervised image classification and segmentation
- Abstract:
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We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match semantic classes, achieving state-of-the-art results in eight unsupervised clustering benchmarks spanning image classification and segmentation. These include STL10, an unsupervised variant of ImageNet, and CIFAR10, where we significantly beat the accuracy of our closest competitors by 6.6 and 9.5 absolute percenta...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
-
-
(Accepted manuscript, pdf, 4.4MB)
-
- Publisher copy:
- 10.1109/ICCV.2019.00996
- Publication website:
- https://ieeexplore.ieee.org/xpl/conhome/8972782/proceeding
Authors
Funding
Bibliographic Details
- Publisher:
- IEEE Publisher's website
- Host title:
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Pages:
- 9864-9873
- Publication date:
- 2020-02-27
- Acceptance date:
- 2019-07-22
- Event title:
- International Conference on Computer Vision (ICCV)
- Event location:
- Seoul, Korea
- Event website:
- http://iccv2019.thecvf.com
- Event start date:
- 2019-10-27
- Event end date:
- 2019-11-02
- DOI:
- EISSN:
-
2380-7504
- EISBN:
- 978-1-7281-4803-8
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
pubs:1060084
- UUID:
-
uuid:b7abbed8-9223-45af-9764-67853b15d4a2
- Local pid:
- pubs:1060084
- Source identifiers:
-
1060084
- Deposit date:
- 2019-10-04
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2020
- Rights statement:
- © 2019 IEEE.
- Notes:
- This is the accepted manuscript version of the paper. The final version is available online from IEEE at: https://doi.org/10.1109/ICCV.2019.00996
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