Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Topology-Based 3D Reconstruction of Mid-Level Primitives in Man-Made Environments Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Associative Deep Clustering: Training a Classification Network with No Labels

verfasst von : Philip Haeusser, Johannes Plapp, Vladimir Golkov, Elie Aljalbout, Daniel Cremers

Erschienen in: Pattern Recognition

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

We propose a novel end-to-end clustering training schedule for neural networks that is direct, i.e. the output is a probability distribution over cluster memberships. A neural network maps images to embeddings. We introduce centroid variables that have the same shape as image embeddings. These variables are jointly optimized with the network’s parameters. This is achieved by a cost function that associates the centroid variables with embeddings of input images. Finally, an additional layer maps embeddings to logits, allowing for the direct estimation of the respective cluster membership. Unlike other methods, this does not require any additional classifier to be trained on the embeddings in a separate step. The proposed approach achieves state-of-the-art results in unsupervised classification and we provide an extensive ablation study to demonstrate its capabilities.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Topology-Based 3D Reconstruction of Mid-Level Primitives in Man-Made Environments
Nächstes Kapitel A Table Tennis Robot System Using an Industrial KUKA Robot Arm
Fußnoten
Literatur
1.
Aljalbout, E., Golkov, V., Siddiqui, Y., Cremers, D.: Clustering with deep learning: taxonomy and new methods. arXiv preprint arXiv:​1801.​07648 (2018)
2.
Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 215–223 (2011)
3.
Dizaji, K.G., Herandi, A., Huang, H.: Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization. arXiv preprint arXiv:​1704.​06327 (2017)
4.
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1422–1430 (2015)
5.
Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758–2766 (2015)
6.
Dosovitskiy, A., Fischer, P., Springenberg, J.T., Riedmiller, M., Brox, T.: Discriminative unsupervised feature learning with exemplar convolutional neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(9), 1734–1747 (2016)CrossRef
7.
Dosovitskiy, A., Springenberg, J.T., Riedmiller, M., Brox, T.: Discriminative unsupervised feature learning with convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 766–774 (2014)
8.
Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: Advances in Neural Information Processing Systems, pp. 2366–2374 (2014)
9.
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
10.
11.
Haeusser, P., Frerix, T., Mordvintsev, A., Cremers, D.: Associative domain adaptation. In: IEEE International Conference on Computer Vision (ICCV) (2017)
12.
Haeusser, P., Mordvintsev, A., Cremers, D.: Learning by association - a versatile semi-supervised training method for neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
13.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
14.
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRef
15.
Hsu, C.C., Lin, C.W.: CNN-based joint clustering and representation learning with feature drift compensation for large-scale image data. arXiv preprint arXiv:​1705.​07091 (2017)
16.
Hu, W., Miyato, T., Tokui, S., Matsumoto, E., Sugiyama, M.: Learning discrete representations via information maximizing self augmented training. arXiv preprint arXiv:​1702.​08720 (2017)
17.
Huang, C., Change Loy, C., Tang, X.: Unsupervised learning of discriminative attributes and visual representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5175–5184 (2016)
18.
Huang, P., Huang, Y., Wang, W., Wang, L.: Deep embedding network for clustering. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 1532–1537. IEEE (2014)
19.
20.
Krause, A., Perona, P., Gomes, R.G.: Discriminative clustering by regularized information maximization. In: Advances in Neural Information Processing Systems, pp. 775–783 (2010)
21.
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)
22.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference of Neural Information Processing Systems (2012)
23.
24.
Lee, D.H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, vol. 3, p. 2 (2013)
25.
Li, F., Qiao, H., Zhang, B., Xi, X.: Discriminatively boosted image clustering with fully convolutional auto-encoders. arXiv preprint arXiv:​1703.​07980 (2017)
26.
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1, pp. 281–297 (1967)
27.
Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4040–4048 (2016)
28.
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS workshop on deep learning and unsupervised feature learning, vol. 2011, p. 5 (2011)
29.
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:​1511.​06434 (2015)
30.
31.
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. arXiv preprint arXiv:​1606.​03498 (2016)
32.
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
33.
Springenberg, J.T.: Unsupervised and semi-supervised learning with categorical generative adversarial networks. arXiv preprint arXiv:​1511.​06390 (2015)
34.
Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)CrossRef
35.
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
36.
Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: Advances in Neural Information Processing Systems, pp. 2553–2561 (2013)
37.
Toshev, A., Szegedy, C.: DeepPose: human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653–1660 (2014)
38.
Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning, pp. 478–487 (2016)
39.
Yang, B., Fu, X., Sidiropoulos, N.D., Hong, M.: Towards k-means-friendly spaces: simultaneous deep learning and clustering. arXiv preprint arXiv:​1610.​04794 (2016)
40.
Yang, J., Parikh, D., Batra, D.: Joint unsupervised learning of deep representations and image clusters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5147–5156 (2016)
41.
Zheng, Y., Tan, H., Tang, B., Zhou, H., et al.: Variational deep embedding: a generative approach to clustering. arXiv preprint arXiv:​1611.​05148 (2016)
Metadaten
Titel
Associative Deep Clustering: Training a Classification Network with No Labels
verfasst von
Philip Haeusser
Johannes Plapp
Vladimir Golkov
Elie Aljalbout
Daniel Cremers
Copyright-Jahr
2019
Buch
Pattern Recognition

Print ISBN: 978-3-030-12938-5

Electronic ISBN: 978-3-030-12939-2

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-12939-2_2