14.1.4.6 Data Augmentation, Generative Network, Convolutional Network

Chapter Contents (Back)
Small Sample Size. Data Augmentation. Not enough samples, augment the set. Automatic generation.

Lin, T.I.[Tsung I.], Lee, J.C.[Jack C.], Ho, H.J.[Hsiu J.],
On fast supervised learning for normal mixture models with missing information,
PR(39), No. 6, June 2006, pp. 1177-1187.
Elsevier DOI Bayesian classifier; Data augmentation; EM algorithm; Incomplete features; Rao-Blackwellization 0604
BibRef

Zhang, X.F.[Xue-Feng], Chen, B.[Bo], Liu, H.W.[Hong-Wei], Zuo, L.[Lei], Feng, B.[Bo],
Infinite max-margin factor analysis via data augmentation,
PR(52), No. 1, 2016, pp. 17-32.
Elsevier DOI 1601
Latent variable support vector machine BibRef

Lu, J.[Jiang], Li, J.[Jin], Yan, Z.[Ziang], Mei, F.H.[Feng-Hua], Zhang, C.S.[Chang-Shui],
Attribute-Based Synthetic Network (ABS-Net): Learning more from pseudo feature representations,
PR(80), 2018, pp. 129-142.
Elsevier DOI 1805
Pseudo feature representation, Zero-shot learning, Supervised learning, Data augmentation, Attribute learning BibRef


Pang, K.K.[Kun-Kun], Dong, M.Z.[Ming-Zhi], Wu, Y.[Yang], Hospedales, T.M.[Timothy M.],
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice,
ICPR18(2269-2276)
IEEE DOI 1812
Heuristic algorithms, Uncertainty, Prediction algorithms, Switches, Training, Tuning, Pattern recognition BibRef

Beluch, W.H.[William H.], Genewein, T.[Tim], Nurnberger, A.[Andreas], Kohler, J.M.[Jan M.],
The Power of Ensembles for Active Learning in Image Classification,
CVPR18(9368-9377)
IEEE DOI 1812
Uncertainty, Neural networks, Labeling, Training, Monte Carlo methods, Data models BibRef

Gasparetto, A., Ressi, D., Bergamasco, F., Pistellato, M., Cosmo, L., Boschetti, M., Ursella, E., Albarelli, A.,
Cross-Dataset Data Augmentation for Convolutional Neural Networks Training,
ICPR18(910-915)
IEEE DOI 1812
feedforward neural nets, learning (artificial intelligence), neural nets, convolutional neural networks training, Transforms BibRef

Shi, H., Wang, L., Ding, G., Yang, F., Li, X.,
Data Augmentation with Improved Generative Adversarial Networks,
ICPR18(73-78)
IEEE DOI 1812
Generative adversarial networks, Training, Generators, Neural networks, Task analysis, Stochastic processes BibRef

Liu, X., Zou, Y., Kong, L., Diao, Z., Yan, J., Wang, J., Li, S., Jia, P., You, J.,
Data Augmentation via Latent Space Interpolation for Image Classification,
ICPR18(728-733)
IEEE DOI 1812
Interpolation, Training, Training data, Neural networks, Generative adversarial networks, inter-class sampling BibRef

Huang, S.W.[Sheng-Wei], Lin, C.T.[Che-Tsung], Chen, S.P.[Shu-Ping], Wu, Y.Y.[Yen-Yi], Hsu, P.H.[Po-Hao], Lai, S.H.[Shang-Hong],
AugGAN: Cross Domain Adaptation with GAN-Based Data Augmentation,
ECCV18(IX: 731-744).
Springer DOI 1810
BibRef

Liu, B.[Bo], Wang, X.D.[Xu-Dong], Dixit, M.[Mandar], Kwitt, R.[Roland], Vasconcelos, N.[Nuno],
Feature Space Transfer for Data Augmentation,
CVPR18(9090-9098)
IEEE DOI 1812
Trajectory, Manifolds, Feature extraction, Task analysis, Shape, Decoding BibRef

Merchant, A., Syed, T., Khan, B., Munir, R.,
Appearance-based data augmentation for image datasets using contrast preserving sampling,
ICPR18(1235-1240)
IEEE DOI 1812
Kernel, Convolutional neural networks, Shape, Tensile stress, Error analysis, Agriculture, Data models, constraint graph BibRef

Elezi, I., Torcinovich, A., Vascon, S., Pelillo, M.,
Transductive Label Augmentation for Improved Deep Network Learning,
ICPR18(1432-1437)
IEEE DOI 1812
Games, Labeling, Standards, Neural networks, Computer vision, Feature extraction, Training BibRef

Nguyen, T.D., Nguyen, V., Le, T., Phung, D.,
Distributed data augmented support vector machine on Spark,
ICPR16(498-503)
IEEE DOI 1705
Data models, Distributed databases, Estimation, Industries, Scalability, Sparks, Support vector machines, Apache Spark, big data, distributed computing, large-scale classification, support, vector, machine BibRef

d'Innocente, A.[Antonio], Carlucci, F.M.[Fabio Maria], Colosi, M.[Mirco], Caputo, B.[Barbara],
Bridging Between Computer and Robot Vision Through Data Augmentation: A Case Study on Object Recognition,
CVS17(384-393).
Springer DOI 1711
BibRef

Wong, S.C., Gatt, A., Stamatescu, V., McDonnell, M.D.,
Understanding Data Augmentation for Classification: When to Warp?,
DICTA16(1-6)
IEEE DOI 1701
BibRef

Fawzi, A.[Alhussein], Samulowitz, H.[Horst], Turaga, D.[Deepak], Frossard, P.[Pascal],
Adaptive data augmentation for image classification,
ICIP16(3688-3692)
IEEE DOI 1610
Approximation algorithms. Adding more samples. BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Metric Learning .


Last update:Mar 2, 2019 at 12:07:42