Computer vision and pattern recognition is becoming essential for thousands of practical applications. For example, the future of search engines lies in image and video recognition as opposed to traditional text search. Autonomous robots such as driverless cars depend on it, too. It even has lifesaving impact through medical applications such as cancer detection.

Since 2009, our Deep Learning team has won 9 (nine) first prizes in important and highly competitive international pattern recognition contests (with test set known only to the organisers), far more than any other team. Our neural nets also were the first really Deep Learners to win such contests (e.g., on image classification, object detection, image segmentation), and the first machine learning methods to reach superhuman performance in such a contest. Here the list of won competitions (details in the rightmost column):

9. MICCAI 2013 Grand Challenge on Mitosis Detection
8. ICPR 2012 Contest on Mitosis Detection in Breast Cancer Histological Images
7. ISBI 2012 Brain Image Segmentation Challenge (with superhuman pixel error rate)
6. IJCNN 2011 Traffic Sign Recognition Competition (only our method achieved superhuman results)
5. ICDAR 2011 offline Chinese Handwriting Competition
4. Online German Traffic Sign Recognition Contest
3. ICDAR 2009 Arabic Connected Handwriting Competition
2. ICDAR 2009 Handwritten Farsi/Arabic Character Recognition Competition
1. ICDAR 2009 French Connected Handwriting Competition. Compare the overview page on handwriting recognition.

Our deep learning methods also set records in important Machine Learning (ML) benchmarks (details in the rightmost column):

A. The NORB Object Recognition Benchmark
B. The CIFAR Image Classification Benchmark
C. The MNIST Handwritten Digits Benchmark (perhaps the most famous benchmark; we achieved the 1st human-competitive result in 2011)
D. Chinese characters from the ICDAR 2013 competition (3755 classes)
E. The Weizmann & KTH Human Action Recognition Benchmarks

Remarkably, none of 1-9 & A-D above required the traditional sophisticated computer vision techniques developed over the past six decades or so. Instead, our biologically rather plausible systems are inspired by human brains, and learn to recognize objects from numerous training examples. We use deep, artificial, supervised, feedforward or recurrent (deep by nature) neural networks with many non-linear processing stages.

We started work on "deep learning" over two decades ago. Back then, Sepp Hochreiter (now professor) was an undergrad student working on Schmidhuber's neural net project. His 1991 thesis (PDF) is a deep learning milestone: it formally showed that deep networks like the above are hard to train because they suffer from the now famous problem of vanishing or exploding gradients. Since then we have developed various techniques to overcome this obstacle (e.g., see here). In 1991 we used a stack of recurrent neural networks (RNN) pre-trained in unsupervised fashion to compactly encode input sequences, where lower layers of the hierarchy learn to extract compact sequence representations fed to higher layers. This can greatly accelerate subsequent supervised learning. See J. Schmidhuber, Learning complex, extended sequences using the principle of history compression, Neural Computation, 4(2):234-242, 1992, PDF. (Based on TR FKI-148-91, 1991.) See Schmidhuber's Habilitation thesis (TUM, 1993) for an experiment with 1200 nonlinear virtual layers. The 1991 system also was the first Neural Hierarchical Temporal Memory.

Today we use graphics cards or GPUs (mini-supercomputers for video games, see picture in 2nd column) to speed up learning by a factor of up to 50. Our committees of networks improve the results even further.

Note that the successes 1-9 & A-D above did NOT require any unsupervised pre-training, which is a bit depressing, as we have developed unsupervised learning algorithms for 20 years. However, our new systems' feature detectors (FDs) do resemble those found by our old unsupervised methods such as Predictability Minimization (1992; more: 1996) and Low-Complexity Coding and Decoding (LOCOCODE, 1999).

FD due to semi- linear PM (1992), made in 1996

Reference [14] also uses fast deep nets, this time to achieve superior hand gesture recognition. And reference [16] uses them to achieve superior steel defect detection, three times better than support vector machines (SVM) trained on commonly used feature descriptors.

Not all of our pattern recognizers use neural nets though. For E above, novel supervised and unsupervised kernel-based methods were employed. Reference [5] uses credal classifiers to classify textures. And reference [2] uses a fast voting scheme to answer image-based queries, successfully tested on the ZuBud database.

For information on how we have built on the work of earlier pioneers since the 1960s, please visit www.deeplearning.me.

Copyright notice (2011): Fibonacci web design by Jürgen Schmidhuber, who will be delighted if you use this web page for educational and non-commercial purposes, including articles for Wikipedia and similar sites, provided you mention the source and provide a link. The Deep Learning site derives from the present page.

Last update 2014
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Computer Vision Team (ex-)members in Schmidhuber's lab(s): Dan Ciresan, Ueli Meier, Jonathan Masci, Somayeh Danafar, Alex Graves, Davide Migliore. For medical imaging, we also work with Alessandro Giusti in the group of Luca Maria Gambardella.

Our work builds on earlier work by great neural network pioneers including Bryson, Kelley, Dreyfus, Werbos, Fukushima, Amari, LeCun, Hinton, Williams, Rumelhart, Poggio, von der Malsburg, Kohonen, and others (more).

SELECTED PUBLICATIONS

[24] J. Schmidhuber. Deep Learning in Neural Networks: An Overview. Neural Networks, Volume 61, January 2015, Pages 85-117, published online in 2014 (DOI: 10.1016/ j.neunet.2014.09.003). Draft of invited survey (88 pages, 888 references): Preprint IDSIA-03-14 / arXiv:1404.7828 [cs.NE]; version v4 (PDF, 8 Oct 2014); LATEX source; complete public BIBTEX file (888 kB). (Older PDF versions: v1 of 30 April; v1.5 of 15 May; v2 of 28 May; v3 of 2 July.) HTML overview page.

[23] D. Ciresan, J. Schmidhuber. Multi-Column Deep Neural Networks for Offline Handwritten Chinese Character Classification. Preprint arXiv:1309.0261, 1 Sep 2013.

[22] J. Masci, A. Giusti, D. Ciresan, G. Fricout, J. Schmidhuber. A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional Networks. ICIP 2013. Preprint arXiv:1302.1690. On object detection in large images, now scanned by our deep networks 1500 times faster than with previous methods.

[21] A. Giusti, D. Ciresan, J. Masci, L.M. Gambardella, J. Schmidhuber. Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks. ICIP 2013. Preprint arXiv:1302.1700

[20] D. Ciresan, A. Giusti, L. M. Gambardella, J. Schmidhuber. Mitosis Detection in Breast Cancer Histology Images using Deep Neural Networks. MICCAI 2013. PDF.

[19] D. Ciresan, U. Meier, J. Schmidhuber. Transfer Learning for Latin and Chinese Characters with Deep Neural Networks. Proc. IJCNN 2012, p 1301-1306, 2012. PDF. Pretrain on one data set, profit on another.

[18] D. C. Ciresan, U. Meier, J. Masci, J. Schmidhuber. Multi-Column Deep Neural Network for Traffic Sign Classification. Neural Networks 32, p 333-338, 2012. PDF of preprint. (First superhuman visual pattern recognition.)

[17] D. C. Ciresan, U. Meier, J. Schmidhuber. Multi-column Deep Neural Networks for Image Classification. IEEE Conf. on Computer Vision and Pattern Recognition CVPR 2012, p 3642-3649, 2012. PDF. Longer preprint arXiv:1202.2745v1 [cs.CV].

[16] J. Masci, U. Meier, D. Ciresan, G. Fricout, J. Schmidhuber. Steel Defect Classification with Max-Pooling Convolutional Neural Networks. Proc. IJCNN 2012. PDF.

[15] D. Ciresan, A. Giusti, L. Gambardella, J. Schmidhuber. Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images. In Advances in Neural Information Processing Systems (NIPS 2012), Lake Tahoe, 2012. PDF. (See also ISBI EM Competition Abstracts.)

[14] J. Nagi, F. Ducatelle, G. A. Di Caro, D. Ciresan, U. Meier, A. Giusti, F. Nagi, J. Schmidhuber, L. M. Gambardella. Max-Pooling Convolutional Neural Networks for Vision-based Hand Gesture Recognition. Proc. 3rd IEEE Intl. Conf. on Signal & Image Processing and Applications (ICSIPA), Kuala Lumpur, 2011. PDF.

[13] J. Schmidhuber, D. Ciresan, U. Meier, J. Masci, A. Graves. On Fast Deep Nets for AGI Vision. In Proc. Fourth Conference on Artificial General Intelligence (AGI-11), Google, Mountain View, California, 2011. PDF. Video.

[12] D. C. Ciresan, U. Meier, L. M. Gambardella, J. Schmidhuber. Convolutional Neural Network Committees For Handwritten Character Classification. 11th International Conference on Document Analysis and Recognition (ICDAR 2011), Beijing, China, 2011. PDF.

[11] U. Meier, D. C. Ciresan, L. M. Gambardella, J. Schmidhuber. Better Digit Recognition with a Committee of Simple Neural Nets. 11th International Conference on Document Analysis and Recognition (ICDAR 2011), Beijing, China, 2011. PDF.

[10] D. C. Ciresan, U. Meier, J. Masci, J. Schmidhuber. A Committee of Neural Networks for Traffic Sign Classification. International Joint Conference on Neural Networks (IJCNN-2011, San Francisco), 2011. PDF.

[9] D. C. Ciresan, U. Meier, L. M. Gambardella, J. Schmidhuber. Handwritten Digit Recognition with a Committee of Deep Neural Nets on GPUs. ArXiv Preprint arXiv:1103.4487v1 [cs.LG], 23 Mar 2011.

[8] D. C. Ciresan, U. Meier, J. Masci, L. M. Gambardella, J. Schmidhuber. Flexible, High Performance Convolutional Neural Networks for Image Classification. International Joint Conference on Artificial Intelligence (IJCAI-2011, Barcelona), 2011. PDF. ArXiv preprint, 1 Feb 2011. Describes our special breed of max-pooling convolutional networks (MPCNN), now widely tested/used by research labs (e.g., Univ. Toronto/Google/Stanford) and companies (e.g., Apple) all over the world.

[7] S. Danafar, A. Giusti, J. Schmidhuber. New State-of-the-Art Recognizers of Human Actions. EURASIP Journal on Advances in Signal Processing, doi:10.1155/2010/202768, 2010. HTML.

[6] D. C. Ciresan, U. Meier, L. M. Gambardella, J. Schmidhuber. Deep Big Simple Neural Nets For Handwritten Digit Recognition. Neural Computation 22(12): 3207-3220, 2010. ArXiv Preprint arXiv:1003.0358v1 [cs.NE], 1 March 2010.

[5] G. Corani, A. Giusti, D. Migliore, J. Schmidhuber. Robust Texture Recognition Using Credal Classifiers. Proc. BMVC, p 78.1-78.10. BMVA Press, 2010. doi:10.5244/C.24.78. HTML.

[4] A. Graves, J. Schmidhuber. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks. Advances in Neural Information Processing Systems 22, NIPS'22, p 545-552, Vancouver, MIT Press, 2009. PDF.

[3] A. Graves, S. Fernandez, J. Schmidhuber. Multi-Dimensional Recurrent Neural Networks. Intl. Conf. on Artificial Neural Networks ICANN'07, 2007. Preprint: arxiv:0705.2011. PDF.

[3a] A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber. Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. ICML 06, Pittsburgh, 2006. PDF.

[2] M.v.d. Giessen and J. Schmidhuber. Fast color-based object recognition independent of position and orientation. In W. Duch et al. (Eds.): Proc. Intl. Conf. on Artificial Neural Networks ICANN'05, LNCS 3696, pp. 469-474, Springer-Verlag Berlin Heidelberg, 2005. PDF.

Ongoing work on active perception. While the methods above tend to work fine in many applications, they are passive learners - they do not learn to actively search for the most informative image parts. Humans, however, use sequential gaze shifts for pattern recognition. This can be much more efficient than the fully parallel one-shot approach. That's why we want to combine the algorithms above with variants of our old method of 1990 - back then we built what to our knowledge was the first artificial fovea sequentially steered by a learning neural controller. Without a teacher, it used a variant of reinforcement learning to create saccades and find targets in a visual scene (and to track moving targets), although computers were a million times slower back then:

[1] J. Schmidhuber, R. Huber. Learning to generate artificial fovea trajectories for target detection. International Journal of Neural Systems, 2(1 & 2):135-141, 1991 (figures omitted). PDF. HTML. HTML overview with figures.

More on active learning without a teacher in the overview pages on the Formal Theory of Creativity and Curiosity.

More Deep Learning Web Sites:

Deep Learning since 1991 (overview site derived from the present page)

Sept/Oct 2013: G+ posts on Deep Learning

Deep NN win MICCAI 2013 Grand Challenge and 2012 ICPR Contest on Mitosis Detection (first Deep Learner to win a contest on object detection in large images)

Deep NN win 2012 Brain Image Segmentation Contest (first image segmentation competition won by a feedforward Deep Learner)

2012: 8th international pattern recognition contest won since 2009

2011: First superhuman visual pattern recognition (twice better than humans, three times better than the closest artificial competitor, six times better than the best non-neural method)

2009: First official international pattern recognition contests won by Deep Learning (connected handwriting through LSTM RNN: simultaneous segmentation and recognition)

1997: First purely supervised Deep Learner (LSTM RNN)

JS' first Deep Learner of 1991 + Deep Learning Timeline 1962-2013 (also summarises the origins of backpropagation, still the central algorithm of Deep Learning)

1991: Fundamental Deep Learning Problem discovered and analysed and partially solved
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