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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359
Deep Learning Face Attributes in the Wild
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747
Self-citationContour Detection and Hierarchical Image Segmentation
Arbeláez, P., Maire, M., Fowlkes, C., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,454
Dimensionality Reduction by Learning an Invariant Mapping
Hadsell, R., Chopra, S., & LeCun, Y. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.100 · Cited by 5,336
Borderline-SMOTE : A New Over-Sampling Method in Imbalanced Data Sets Learning
Han, H., Wang, W.-Y., & Mao, B.-H. (2005) · Lecture notes in computer science · doi:10.1007/11538059_91 · Cited by 4,331
Distance Metric Learning for Large Margin Nearest Neighbor Classification
Weinberger, K. Q., & Saul, L. K. (2009) · Journal of Machine Learning Research · doi:10.5555/1577069.1577078 · Cited by 4,044
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150 · cited 5×
“Cl as s i m ba la nc e le ve l ( % ) Face attribute Over PANDA [32] Over Triplet-kNN [22]”
BackgroundInfluentialLocality-constrained Linear Coding for image classification
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540018 · Cited by 3,100
Holistically-Nested Edge Detection
Xie, S., & Tu, Z. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.164 · Cited by 3,096
Why Does Unsupervised Pre-training Help Deep Learning?
Erhan, D., Bengio, Y., Courville, A. C., Manzagol, P.-A., Vincent, P., & Samy Bengio, A. (2010) · Cited by 2,178
Deep Learning Face Representation by Joint Identification-Verification
Sun, Y., Wang, X., & Tang, X. (2014) · arXiv · doi:10.48550/arxiv.1406.4773 · Cited by 1,906
Self-citationAttribute and simile classifiers for face verification
Kumar, N., Berg, A. C., Belhumeur, P. N., & Nayar, S. K. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459250 · Cited by 1,361
Training cost-sensitive neural networks with methods addressing the class imbalance problem
Zhou, Z.-H., & Liu, X.-Y. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.17 · Cited by 1,326
Learning Fine-Grained Image Similarity with Deep Ranking
Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., & Wu, Y. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.180 · Cited by 1,269
Multiscale Combinatorial Grouping
Arbelaez, P., Pont-Tuset, J., Barron, J., Marques, F., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.49 · Cited by 1,155
An empirical evaluation of deep architectures on problems with many factors of variation
Larochelle, H., Erhan, D., Courville, A., Bergstra, J., & Bengio, Y. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273556 · Cited by 992
Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data
Khan, S. H., Hayat, M., Bennamoun, M., Sohel, F. A., & Togneri, R. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2732482 · Cited by 967
SVMs Modeling for Highly Imbalanced Classification
Tang, Y., Zhang, Y.-Q., Chawla, N. V., & Krasser, S. (2009) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2008.2002909 · Cited by 966
Fast Edge Detection Using Structured Forests
Dollar, P., & Zitnick, C. L. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2377715 · Cited by 962
C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
Drummond, C., & Holte, R. C. (2003) · Cited by 799
Cost-sensitive learning by cost-proportionate example weighting
Zadrozny, B., Langford, J., & Abe, N. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250950 · Cited by 680
Optimised KD-trees for fast image descriptor matching
Silpa-Anan, C., & Hartley, R. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587638 · Cited by 677
Metric Learning by Collapsing Classes
Globerson, A., & Roweis, S. T. (2005) · Cited by 665
DeepEdge: A multi-scale bifurcated deep network for top-down contour detection
Bertasius, G., Shi, J., & Torresani, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299067 · Cited by 525
DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection
Shen, W., Wang, X., Wang, Y., Bai, X., & Zhang, Z. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299024 · Cited by 515
PANDA: Pose Aligned Networks for Deep Attribute Modeling
Zhang, N., Paluri, M., Ranzato, M., Darrell, T., & Bourdev, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.212 · Cited by 495
Describable Visual Attributes for Face Verification and Image Search
Kumar, N., Berg, A., Belhumeur, P. N., & Nayar, S. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.48 · Cited by 434
Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection
Lim, J. J., Zitnick, C. L., & Dollar, P. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.406 · Cited by 427
Using Random Forest to Learn Imbalanced Data
Chen, C., Liaw, A., & Breiman, L. (2004) · UC Berkeley · Cited by 393
Berg, T., & Belhumeur, P. N. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.128 · Cited by 372
Ask the locals: Multi-way local pooling for image recognition
Boureau, Y.-L., Le Roux, N., Bach, F., Ponce, J., & LeCun, Y. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126555 · Cited by 277
A Comparative Study of Cost-Sensitive Boosting Algorithms
Ting, K. M. (2000) · Cited by 262
$$N^4$$-Fields: Neural Network Nearest Neighbor Fields for Image Transforms
Ganin, Y., & Lempitsky, V. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-16808-1_36 · Cited by 261
Local neighbourhood extension of SMOTE for mining imbalanced data
Maciejewski, T., & Stefanowski, J. (2011) · 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) · doi:10.1109/cidm.2011.5949434 · Cited by 255
Discriminatively Trained Sparse Code Gradients for Contour Detection
Ren, X., & Bo, L. (2012) · Cited by 234
Crisp Boundary Detection Using Pointwise Mutual Information
Isola, P., Zoran, D., Krishnan, D., & Adelson, E. H. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_52 · Cited by 199
Bertasius, G., Shi, J., & Torresani, L. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.65 · Cited by 189
Classification of Imbalanced Data by Combining the Complementary Neural Network and SMOTE Algorithm
Jeatrakul, P., Wong, K. W., & Fung, C. C. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-17534-3_19 · Cited by 185
Oriented edge forests for boundary detection
Hallman, S., & Fowlkes, C. C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298782 · Cited by 177
Discriminative Transfer Learning with Tree-based Priors
Srivastava, N., & Salakhutdinov, R. (2013) · Cited by 165
Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets
Liu, W., & Chawla, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20847-8_29 · Cited by 157
An Online Algorithm for Large Scale Image Similarity Learning
Chechik, G., Shalit, U., Sharma, V., & Bengio, S. (2009) · Cited by 94
A Deep Non-linear Feature Mapping for Large-Margin kNN Classification
Min, R., Stanley, D. A., Yuan, Z., Bonner, A. J., & Zhang, Z. (2009) · Proceedings · doi:10.1109/icdm.2009.27 · Cited by 83
Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection
Kivinen, J., Williams, C. K. I., & Heess, N. (2014) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 76
Discriminative Sparse Neighbor Approximation for Imbalanced Learning
Huang, C., Loy, C. C., & Tang, X. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2671845 · Cited by 34
Self-citation
Cited by
Class-Balanced Loss Based on Effective Number of Samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., & Belongie, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00949 · Cited by 3,094
Survey on deep learning with class imbalance
Johnson, J. M., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0192-5 · Cited by 3,032
Deep learning and its applications to machine health monitoring
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.05.050 · Cited by 2,894
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
Huang, L., Zhao, X., & Huang, K. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2957464 · Cited by 1,809
Learning a Unified Classifier Incrementally via Rebalancing
Hou, S., Pan, X., Loy, C. C., Wang, Z., & Lin, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00092 · Cited by 1,195
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.
