The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Learning Deep Representation for Imbalanced Classification
Abstract
Data in vision domain often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary classification methods based on deep convolutional neural network (CNN) typically follow classic strategies such as class re-sampling or cost-sensitive training. In this paper, we conduct extensive and systematic experiments to validate the effectiveness of these classic schemes for representation learning on class-imbalanced data. We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain both intercluster and inter-class margins. This tighter constraint effectively reduces the class imbalance inherent in the local data neighborhood. We show that the margins can be easily deployed in standard deep learning framework through quintuplet instance sampling and the associated triple-header hinge loss. The representation learned by our approach, when combined with a simple k-nearest neighbor (kNN) algorithm, shows significant improvements over existing methods on both high-and low-level vision classification tasks that exhibit imbalanced class distribution.
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Cites
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Cited by
Class-Balanced Loss Based on Effective Number of Samples
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Deep learning and its applications to machine health monitoring
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GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
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Learning a Unified Classifier Incrementally via Rebalancing
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Self-citation
Metadata
- Authors
- Huang, C., Li, Y., Loy, C. C., & Tang, X.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 5375-5384
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2440599146
- Cited by
- 988
- References
- 48
- FWCI
- 27.95
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Huang, C., Li, Y., Loy, C. C., & Tang, X. (2016). Learning Deep Representation for Imbalanced Classification. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.580
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