CoChat
Sign Up
Cited by 472Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

The DOI matches its Crossref record; 27 of the 32 references it cites were found in a registry or an index.

Generative OpenMax for Multi-Class Open Set Classification

Open access
Ge, Zongyuan, Demyanov, Sergey, Garnavi, Rahil·2017·Procedings of the British Machine Vision Conference 2017
DOI 10.5244/c.31.42
Open this paper in CoChat — free accountOpen PDF

Abstract

We present a conceptually new and flexible method for multi-class open set classification. Unlike previous methods where unknown classes are inferred with respect to the feature or decision distance to the known classes, our approach is able to provide explicit modelling and decision score for unknown classes. The proposed method, called Gener- ative OpenMax (G-OpenMax), extends OpenMax by employing generative adversarial networks (GANs) for novel category image synthesis. We validate the proposed method on two datasets of handwritten digits and characters, resulting in superior results over previous deep learning based method OpenMax Moreover, G-OpenMax provides a way to visualize samples representing the unknown classes from open space. Our simple and effective approach could serve as a new direction to tackle the challenging multi-class open set classification problem.

Overview

Integrity signals

Registry

Crossref · 10.5244/c.31.42

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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 · cited 1×

    “Driven by recent research in machine learning, the best performing system can achieve impressive results on both small and large scale multi-class classification tasks [8, 9, 13, 21, 28].”
    Background

  • 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 · cited 2×

    “Driven by recent research in machine learning, the best performing system can achieve impressive results on both small and large scale multi-class classification tasks [8, 9, 13, 21, 28].”
    BackgroundMethod

  • 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 · cited 4×

    “We trained the generator G on ImageNet12 and made several observations: 1) Several classes blend into unrealistic type of object in terms of colour and global structure.”
    BackgroundInfluential

  • ImageNet Large Scale Visual Recognition Challenge

    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · cited 1×

    “Driven by recent research in machine learning, the best performing system can achieve impressive results on both small and large scale multi-class classification tasks [8, 9, 13, 21, 28].”
    Background

  • Image-to-Image Translation with Conditional Adversarial Networks

    Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · cited 1×

    “GAN has a wide range of applications including, sketch/text to image synthesis [4], image to image translation [10], image editing [19], super resolution [15] and video prediction [6], etc.”
    Background

  • Auto-Encoding Variational Bayes

    Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · cited 2×

    “The most prominent are variational autoencoder (VAE) [12] and generative adversarial networks (GAN) [7], among which, GAN is a powerful model of learning arbitrarily complex data distributions.”
    Background

  • Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

    Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696 · cited 1×

    “GAN has a wide range of applications including, sketch/text to image synthesis [4], image to image translation [10], image editing [19], super resolution [15] and video prediction [6], etc.”
    Background

  • 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

  • Improved Techniques for Training GANs

    Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965 · cited 2×

    “This is done by using generative adversarial networks (GANs) [7] to generate synthetic samples from unknown classes.”
    Background

  • Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

    Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · cited 1×

    “Driven by recent research in machine learning, the best performing system can achieve impressive results on both small and large scale multi-class classification tasks [8, 9, 13, 21, 28].”
    Background

  • Conditional Image Synthesis With Auxiliary Classifier GANsPreprint

    Odena, A., Olah, C., & Shlens, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09585 · Cited by 3,030 · cited 1×

    “We employ a modified conditional GAN [18] to train the generator and then synthesize unknown classes out of it.”
    Method

  • Wasserstein GAN

    Arjovsky, M., Chintala, S., & Bottou, L. (2017) · arXiv · doi:10.48550/arxiv.1701.07875 · Cited by 2,225

  • Predicting good probabilities with supervised learning

    Niculescu-Mizil, A., & Caruana, R. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102430 · Cited by 1,884 · cited 1×

    “Several recent solutions to multi-class open set problem convert it into a score calibration task [17, 23, 27].”
    Background

  • Towards Open Set Deep Networks

    Bendale, A., & Boult, T. E. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.173 · Cited by 1,799 · cited 6×

    “However, we perfomed the same experiment as in [3] and figured out there are no obvious performance improvement by using G-OpenMax over the natural image setting.”
    BackgroundMethodInfluential

  • Toward Open Set Recognition

    Scheirer, W. J., de Rezende Rocha, A., Sapkota, A., & Boult, T. E. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.256 · Cited by 1,573 · cited 4×

    “In practice, many recent open set works [2, 3, 11, 24, 25] assume that the classes from unknown open space will share some common properties with known classes.”
    BackgroundInfluential

  • How Many Species Are There on Earth?

    May, R. M. (1988) · Science · doi:10.1126/science.241.4872.1441 · Cited by 1,190 · cited 1×

    “Considering only the number of animal species on the earth, the number of classes would exceed 1.5 million [16] animal species on the earth, not to mention other general object categories.”
    BackgroundInfluential

  • Unsupervised Learning for Physical Interaction through Video Prediction

    Finn, C., Goodfellow, I., & Levine, S. (2016) · arXiv · doi:10.48550/arxiv.1605.07157 · Cited by 759 · cited 1×

    “GAN has a wide range of applications including, sketch/text to image synthesis [4], image to image translation [10], image editing [19], super resolution [15] and video prediction [6], etc.”
    Background

  • Probability Models for Open Set Recognition

    Scheirer, W. J., Jain, L. P., & Boult, T. E. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2321392 · Cited by 630 · cited 6×

    “To tackle this challenge, open set classification topic has recently attracted much attention [2, 3, 11, 24, 25].”
    BackgroundMethodInfluential

  • Invertible Conditional GANs for image editing

    Perarnau, G., van de Weijer, J., Raducanu, B., & Álvarez, J. M. (2016) · arXiv · doi:10.48550/arxiv.1611.06355 · Cited by 563 · cited 2×

    “GAN has a wide range of applications including, sketch/text to image synthesis [4], image to image translation [10], image editing [19], super resolution [15] and video prediction [6], etc.”
    Background

  • Towards Open World Recognition

    Bendale, A., & Boult, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298799 · Cited by 529 · cited 3×

    “In practice, many recent open set works [2, 3, 11, 24, 25] assume that the classes from unknown open space will share some common properties with known classes.”
    Background

  • Mask R-CNN

    He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017) · arXiv · doi:10.48550/arxiv.1703.06870 · Cited by 406 · cited 1×

    “Driven by recent research in machine learning, the best performing system can achieve impressive results on both small and large scale multi-class classification tasks [8, 9, 13, 21, 28].”
    Background

  • Multi-class Open Set Recognition Using Probability of Inclusion

    Jain, L. P., Scheirer, W. J., & Boult, T. E. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10578-9_26 · Cited by 368 · cited 4×

    “replication of the experiment performed in [11], where 6 classes are hold out in training and the rest 4 classes are used to varying “openness”.”
    BackgroundMethodInfluential

  • Extreme Value Theory

    Smith, R. L. (2019) · Handbook of Heavy-Tailed Distributions in Asset Management and Risk Management · doi:10.1142/9789813276208_0009 · Cited by 262 · cited 4×

    “The unknown class probabilities are then estimated through statistical modelling, specially Extreme Value Theory (EVT) [26].”
    MethodInfluential

  • Meta-Recognition: The Theory and Practice of Recognition Score Analysis

    Scheirer, W., Rocha, A., Micheals, R., et al. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.54 · Cited by 220 · cited 1×

    “Formalization for open space risk is considered as the relative measure of open space compared to the overall measure space: R open = Unknown Space ( Known Space + Unknown Open Space ) Several recent solutions to multi-class open set problem convert it into a score calibration task [17, 23, 27].”
    Background

  • How many species are there?

    Stork, N. E. (1993) · Biodiversity and Conservation · doi:10.1007/bf00056669 · Cited by 173 · cited 1×

    “5 million [16] animal species on the earth, not to mention other general object categories.”
    Background

  • Adversarial Training for Sketch Retrieval

    Creswell, A., & Bharath, A. (2016) · ECCV Workshops · doi:10.1007/978-3-319-46604-0_55 · Cited by 50 · cited 1×

    “GAN has a wide range of applications including, sketch/text to image synthesis [4], image to image translation [10], image editing [19], super resolution [15] and video prediction [6], etc.”
    Background

  • The HASYv2 dataset

    Thoma, M. (2017) · arXiv.org · doi:10.48550/arxiv.1701.08380 · Cited by 37 · cited 3×

    “We perform a thorough comparison of G-OpenMax to OpenMax and its variants on two datasets MNIST [14] and HASYv2 [29].”
    BackgroundMethod

Flagged first, then by citations

5 of the 32 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

See all 472

Related

See all

Metadata

Authors
Ge, Z., Demyanov, S., & Garnavi, R.
ORCID
Venue
Procedings of the British Machine Vision Conference 2017
Publisher
British Machine Vision Association
Published
2017
DOI
10.5244/c.31.42
OpenAlex
W2963875483
Cited by
472
References
32
Citations by year
2026: 36 · 2025: 59 · 2024: 98 · 2023: 90 · 2022: 63 · 2021: 74 · 2020: 31 · 2019: 12 · 2018: 8 · 2017: 1
FWCI
4.16
Type
conference paper
Language
en
Open access
Locations
  • British Machine Vision Conference (BMVC) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords
open-set recognition, OpenMax, generative adversarial networks, image generation, unknown class detection, handwritten digits, handwritten characters

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