14.5.2 Learning, General Surveys, Overviews

Chapter Contents (Back)
Survey, Learning. Learning. Neural Networks. GAN. CNN.

Schwartz, S.R., Wah, B.W.[Benjamin W.],
Machine Learning of Computer Vision Algorithms,
HPRIP-CV94(319-359). BibRef 9400

Fu, K.S., ed.,
Pattern Recognition and Machine Learning,
PlenumPress, New York, 1971. BibRef 7100

Bhanu, B., Poggio, T.,
Introduction to the Special Section on Learning in Computer Vision,
PAMI(16), No. 9, September 1994, pp. 865-867.
IEEE Top Reference. BibRef 9409

Bhanu, B., Peng, J., Huang, T., Draper, B.,
Introduction to the Special Issue on Learning in Computer Vision and Pattern Recognition,
SMC-B(35), No. 3, June 2005, pp. 391-396.

Vapnik, V.,
The Nature of Statistical Learning Theory,
Springer-Verlag1996. BibRef 9600

Vapnik, V.,
Statistical Learning Theory,
John Wiley& Sons, 1998. BibRef 9800

Vapnik, V.[Vladimir],
An overview of statistical learning theory,
TNN(10), No. 5, 1999, pp. 988-999. 0906

Dietterich, T.G.,
Machine Learning Research: Four Current Directions,
AI Magazine(18), No. 4, 1997, pp. 97-136. BibRef 9700

Poggio, T.[Tomaso], Shelton, C.R.[Christian R.],
Machine Learning, Machine Vision, and the Brain,
AIMag(20), No. 3, Fall 1999, pp. 37-55. Regularization. Support Vector Machines. Survey, Learning. Survey of learning focused on a vision domain. Regularization, Support Vector Machines. Applied to face and pedestrian recognition. BibRef 9900

Petrou, M.[Maria],
Learning in Pattern Recognition: Some Thoughts,
PRL(22), No. 1, January 2001, pp. 3-13.
Elsevier DOI 0105

Petrou, M.[Maria],
Learning in Computer Vision: Some Thoughts,
Springer DOI 0711

Xu, M.[Mai], Petrou, M.[Maria],
3D Scene interpretation by combining probability theory and logic: The tower of knowledge,
CVIU(115), No. 11, November 2011, pp. 1581-1596.
Elsevier DOI 1110
Learning Logic Rules for Scene Interpretation Based on Markov Logic Networks,
ACCV09(III: 341-350).
Springer DOI 0909
Recursive Tower of Knowledge for Learning to Interpret Scenes,
PDF File. 0809
Scene labelling systems; Logic and probabilities; Machine learning; System architecture BibRef

Xu, M.[Mai], Wang, Z.[Zulin], Petrou, M.[Maria],
Tower of Knowledge for scene interpretation: A survey,
PRL(48), No. 1, 2014, pp. 42-48.
Elsevier DOI 1410
Tower of Knowledge. Cue of human language, for scene interpretation BibRef

Freeman, W.T.[William T.], Perona, P.[Pietro], Schölkopf, B.[Bernhard],
Guest Editorial Machine Learning for Vision,
IJCV(77), No. 1-3, May 2008, pp. 1.
Springer DOI 0803

Raducanu, B.[Bogdan], Vitria, J.[Jordi],
Learning to learn: From smart machines to intelligent machines,
PRL(29), No. 8, 1 June 2008, pp. 1024-1032.
Elsevier DOI 0804
Online Learning for Human-Robot Interaction,
Incremental subspace learning based on Nonparametric Discriminant Analysis. Number of classes and samples not known and changes over time. Intelligent systems; Cognitive development; Context; Social robotics; Face recognition BibRef

Raducanu, B.[Bogdan], Vitria, J.[Jordi], Leonardis, A.[Ales],
Online pattern recognition and machine learning techniques for computer-vision: Theory and applications,
IVC(28), No. 7, July 2010, pp. 1063-1064.
Elsevier DOI 1006
Introduction to special issue. BibRef

Ioannidou, A.[Anastasia], Chatzilari, E.[Elisavet], Nikolopoulos, S.[Spiros], Kompatsiaris, I.[Ioannis],
Deep Learning Advances in Computer Vision with 3D Data: A Survey,
Surveys(50), No. 2, June 2017, pp. Article No 20.
DOI Link 1708
Survey, Deep Learning. This article surveys methods applying deep learning on 3D data and provides a classification based on how they exploit them. From the results of the examined works, we conclude that systems employing 2D views of 3D data typically surpass voxel-based (3D) deep models, which however, can perform better with more layers and severe data augmentation. Therefore, larger-scale datasets and increased resolutions are required. BibRef

McCann, M.T., Jin, K.H., Unser, M.,
Convolutional Neural Networks for Inverse Problems in Imaging: A Review,
SPMag(34), No. 6, November 2017, pp. 85-95.
Survey, Convolutional Neural Networks. Computed tomography, Image reconstruction, Image resolution, Image segmentation, Inverse problems, Linear programming, Noise reduction BibRef

Jin, K.H., McCann, M.T., Froustey, E., Unser, M.,
Deep Convolutional Neural Network for Inverse Problems in Imaging,
IP(26), No. 9, September 2017, pp. 4509-4522.
computerised tomography, feedforward neural nets, image resolution, iterative methods, learning (artificial intelligence), medical image processing, BibRef

Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A.,
Deep Reinforcement Learning: A Brief Survey,
SPMag(34), No. 6, November 2017, pp. 26-38.
Survey, Deep Learning. Artificial intelligence, Learning (artificial intelligence), Machine learning, Neural networks, Signal processing algorithms, Visualization BibRef

Fawzi, A.[Alhussein], Moosavi-Dezfooli, S.M.[Seyed-Mohsen], Frossard, P.[Pascal],
The Robustness of Deep Networks: A Geometrical Perspective,
SPMag(34), No. 6, November 2017, pp. 50-62.
Classification, Machine learning, Neural networks, Robustness, Visualization BibRef

Darrell, T.J., Lampert, C., Sebe, N., Wu, Y., Yan, Y.,
Guest Editors' Introduction to the Special Section on Learning with Shared Information for Computer Vision and Multimedia Analysis,
PAMI(40), No. 5, May 2018, pp. 1029-1031.
Collaboration, Computer vision, Information sharing, Learning systems, Machine learning, Multimedia communication, Training data BibRef

Nagy, G.[George],
Document analysis systems that improve with use,
IJDAR(23), No. 1, January 2020, pp. 13-29.
WWW Link. 2003
Estimation, Learning, and Adaptation: Systems That Improve with Use,
Springer DOI 1211
Persistent Issues in Learning and Estimation,
ICPR98(Vol I: 561-564).

Qin, H.T.[Hao-Tong], Gong, R.H.[Rui-Hao], Liu, X.L.[Xiang-Long], Bai, X.[Xiao], Song, J.K.[Jing-Kuan], Sebe, N.[Nicu],
Binary neural networks: A survey,
PR(105), 2020, pp. 107281.
Elsevier DOI 2006
Binary neural network, Deep learning, Model compression, Network quantization, Model acceleration BibRef

Serban, A.[Alex], Poll, E.[Erik], Visser, J.[Joost],
Adversarial Examples on Object Recognition: A Comprehensive Survey,
Surveys(53), No. 3, June 2020, pp. xx-yy.
DOI Link 2007
Survey, Adversairal Networks. security, robustness, machine learning, Adversarial examples BibRef

Goodfellow, I.[Ian], Pouget-Abadie, J.[Jean], Mirza, M.[Mehdi], Xu, B.[Bing], Warde-Farley, D.[David], Ozair, S.[Sherjil], Courville, A.[Aaron], Bengio, Y.[Yoshua],
Generative Adversarial Networks,
CACM(63), No. 11, November 2020, pp. 139-144.
DOI Link 2010
Survey, GAN. BibRef

Wang, Z.W.[Zheng-Wei], She, Q.[Qi], Ward, T.E.[Tomas E.],
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy,
Surveys(54), No. 2, February 2021, pp. xx-yy.
DOI Link 2104
Survey, GAN. Generative adversarial networks, loss-variants, computer vision, stabilizing training, architecture-variants BibRef

Das, R.[Rangan], Sen, S.[Sagnik], Maulik, U.[Ujjwal],
A Survey on Fuzzy Deep Neural Networks,
Surveys(53), No. 3, May 2020, pp. xx-yy.
DOI Link 2007
Survey, Deep Networks. parallel models, integrated models, sequential models, ensemble models, fuzzy systems, Deep architecture BibRef

Wang, H.[Hao], Yeung, D.Y.[Dit-Yan],
A Survey on Bayesian Deep Learning,
Surveys(53), No. 5, September 2020, pp. xx-yy.
DOI Link 2010
generative models, Deep learning, Bayesian networks, probabilistic graphical models BibRef

Samek, W., Montavon, G., Lapuschkin, S., Anders, C.J., Müller, K.R.,
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications,
PIEEE(109), No. 3, March 2021, pp. 247-278.
Survey, Deep Learning. Deep learning, Systematics, Neural networks, Artificial intelligence, Machine learning, Unsupervised learning, neural networks BibRef

Saxena, D.[Divya], Cao, J.N.[Jian-Nong],
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions,
Surveys(54), No. 3, May 2021, pp. xx-yy.
DOI Link 2106
Survey, GAN. computer vision, GANs variants, GANs Survey, Image generation, GANs challenges, GANs, mode collapse, deep Generative models BibRef

Wang, X.H.[Xiao-Han], Eliott, F.M.[Fernanda M.], Ainooson, J.[James], Palmer, J.H.[Joshua H.], Kunda, M.[Maithilee],
An Object is Worth Six Thousand Pictures: The Egocentric, Manual, Multi-image (EMMI) Dataset,
WWW Link. 1802
Dataset, Learning. Egocentric, Manual, Multi-Image (EMMI) Dataset. Automobiles, Cameras, Manuals, Object recognition, Toy manufacturing industry, Training, Visualization BibRef

Bala, J.W., Michalski, R.S., Wnek, J.,
The Prax Approach to Learning a Large Number of Texture Concepts,
AAAI-MLCV93(xx-yy). George Mason University. BibRef 9300

Bala, J.W., Michalski, R.S., and Pachowicz, P.W.,
Progress on Vision through Learning at George Mason University,
ARPA94(I:191-207). BibRef 9400

Michalski, R.S., Rosenfeld, A., Aloimonos, Y., Duric, Z., Maloof, M.A., Zhang, Q.,
Progress on Vision Through Learning,
ARPA96(177-188). BibRef 9600

Bhanu, B.[Bir], Bowyer, K.W.[Kevin W.], Hall, L.O.[Lawrence O.], and Langley, P.[Pat],
Report of the AAAI Fall Symposium on Machine Learning and Computer Vision: What, Why and How?,
ARPA94(I:727-731). BibRef 9400

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Evaluation and Analysis of Learning Techniques .

Last update:Jun 21, 2021 at 13:48:20