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

Liang, J.[Jian], He, R.[Ran], Sun, Z.N.[Zhe-Nan], Tan, T.N.[Tie-Niu],
Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation,
PAMI(41), No. 5, May 2019, pp. 1027-1042.
IEEE DOI 1904
Feature extraction, Training, Task analysis, Kernel, Face, Adaptation models, Benchmark testing, sampling-and-fusion BibRef

Ahmad, M.[Muhammad], Khan, A.[Asad], Khan, A.M.[Adil Mehmood], Mazzara, M.[Manuel], Distefano, S.[Salvatore], Sohaib, A.[Ahmed], Nibouche, O.[Omar],
Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Zhang, Y.[Yinghui], Sun, B.[Bo], Xiao, Y.[Yongkang], Xiao, R.[Rong], Wei, Y.[YunGang],
Feature augmentation for imbalanced classification with conditional mixture WGANs,
SP:IC(75), 2019, pp. 89-99.
Elsevier DOI 1906
Imbalanced classification, Feature augmentation, Generative adversarial nets, Wasserstein distance BibRef

Hou, C.P.[Chen-Ping], Zeng, L.L.[Ling-Li], Hu, D.[Dewen],
Safe Classification with Augmented Features,
PAMI(41), No. 9, Sep. 2019, pp. 2176-2192.
IEEE DOI 1908
Support vector machines, Optimization, Magnetic resonance imaging, Kernel, Testing, Data collection, multi-view learning BibRef

Ke, X., Zou, J., Niu, Y.,
End-to-End Automatic Image Annotation Based on Deep CNN and Multi-Label Data Augmentation,
MultMed(21), No. 8, August 2019, pp. 2093-2106.
IEEE DOI 1908
convolutional neural nets, entropy, feature extraction, image annotation, image retrieval, data augmentation BibRef

Khoreva, A.[Anna], Benenson, R.[Rodrigo], Ilg, E.[Eddy], Brox, T.[Thomas], Schiele, B.[Bernt],
Lucid Data Dreaming for Video Object Segmentation,
IJCV(127), No. 9, September 2019, pp. 1175-1197.
Springer DOI 1908
Generate in-domain training data using the provided annotation on the first frame of each video. BibRef


Carlson, A.[Alexandra], Skinner, K.A.[Katherine A.], Vasudevan, R.[Ram], Johnson-Roberson, M.[Matthew],
Modeling Camera Effects to Improve Visual Learning from Synthetic Data,
VLEASE18(I:505-520).
Springer DOI 1905
learning visual tasks in urban scenes. BibRef

Liu, S.J.[Shuang-Jun], Ostadabbas, S.[Sarah],
A Semi-supervised Data Augmentation Approach Using 3D Graphical Engines,
HBU18(II:395-408).
Springer DOI 1905
BibRef

Milz, S.[Stefan], Rüdiger, T.[Tobias], Süss, S.[Sebastian],
Aerial GANeration: Towards Realistic Data Augmentation Using Conditional GANs,
CVUAV18(II:59-72).
Springer DOI 1905
BibRef

Patel, V., Mujumdar, N., Balasubramanian, P., Marvaniya, S., Mittal, A.,
Data Augmentation Using Part Analysis for Shape Classification,
WACV19(1223-1232)
IEEE DOI 1904
computer vision, convolutional neural nets, feature extraction, image classification, learning (artificial intelligence), Optimization BibRef

Summers, C., Dinneen, M.J.,
Improved Mixed-Example Data Augmentation,
WACV19(1262-1270)
IEEE DOI 1904
learning (artificial intelligence), neural nets, pattern classification, additional training data, Computer science BibRef

Behpour, S., Kitani, K., Ziebart, B.,
ADA: Adversarial Data Augmentation for Object Detection,
WACV19(1243-1252)
IEEE DOI 1904
computational complexity, game theory, learning (artificial intelligence), object detection, Pascal, Object detection 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:Aug 20, 2019 at 20:38:45