14.1.9 Data Augmentation, Generative Network, Convolutional Network

Chapter Contents (Back)
Small Sample Size. Data Augmentation. Augmentation. Not enough samples, augment the set. Automatic generation.
See also Adversarial Networks for Image Synthesis.

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

Yu, F.W.[Fei-Wu], Wu, X.X.[Xin-Xiao], Chen, J.L.[Jia-Lu], Duan, L.X.[Li-Xin],
Exploiting Images for Video Recognition: Heterogeneous Feature Augmentation via Symmetric Adversarial Learning,
IP(28), No. 11, November 2019, pp. 5308-5321.
IEEE DOI 1909
Training, Neural networks, Image recognition, Feature extraction, Generative adversarial networks, image-to-video adaptation BibRef

Deng, T.[Ting], Zeng, Z.G.[Zhi-Gang],
Generate adversarial examples by spatially perturbing on the meaningful area,
PRL(125), 2019, pp. 632-638.
Elsevier DOI 1909
Adversarial attack, Spatial transformation, Grad-CAM BibRef

Zheng, C., Wang, N., Cui, J.,
Hyperspectral Image Classification With Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement,
GeoRS(57), No. 10, October 2019, pp. 7307-7316.
IEEE DOI 1910
feedforward neural nets, geophysical image processing, hyperspectral imaging, image classification, image segmentation, training sample enlargement BibRef

Chiaroni, F., Rahal, M., Hueber, N., Dufaux, F.,
Hallucinating A Cleanly Labeled Augmented Dataset from A Noisy Labeled Dataset Using GAN,
ICIP19(3616-3620)
IEEE DOI 1910
Generative adversarial networks, noisy labeled learning, image classification BibRef

Dupre, R., Fajtl, J., Argyriou, V., Remagnino, P.,
Improving Dataset Volumes and Model Accuracy With Semi-Supervised Iterative Self-Learning,
IP(29), 2020, pp. 4337-4348.
IEEE DOI 2002
Training, Data models, Semisupervised learning, Task analysis, Noise measurement, Deep learning, Solid modeling, Semi-supervised, machine learning BibRef

Beery, S., Liu, Y., Morris, D., Piavis, J., Kapoor, A., Meister, M., Joshi, N., Perona, P.,
Synthetic Examples Improve Generalization for Rare Classes,
WACV20(852-862)
IEEE DOI 2006
Data models, Training, Animals, Cameras, Training data, Atmospheric modeling, Analytical models BibRef

Song, L., Xu, Y., Zhang, L., Du, B., Zhang, Q., Wang, X.,
Learning From Synthetic Images via Active Pseudo-Labeling,
IP(29), 2020, pp. 6452-6465.
IEEE DOI 2007
Task analysis, Data models, Training, Visualization, Adaptation models, Neural networks, Predictive models, object detection BibRef

Pan, X.J.[Xing-Jia], Tang, F.[Fan], Dong, W.M.[Wei-Ming], Gu, Y.[Yang], Song, Z.C.[Zhi-Chao], Meng, Y.P.[Yi-Ping], Xu, P.F.[Peng-Fei], Deussen, O.[Oliver], Xu, C.S.[Chang-Sheng],
Self-Supervised Feature Augmentation for Large Image Object Detection,
IP(29), 2020, pp. 6745-6758.
IEEE DOI 2007
Feature extraction, Object detection, Task analysis, Image resolution, Pipelines, Convolution, Detectors, Self-supervise, large image BibRef

Takahashi, R.[Ryo], Matsubara, T.[Takashi], Uehara, K.[Kuniaki],
Data Augmentation Using Random Image Cropping and Patching for Deep CNNs,
CirSysVideo(30), No. 9, September 2020, pp. 2917-2931.
IEEE DOI 2009
Training, Task analysis, Image color analysis, Principal component analysis, Convolutional neural networks, image-caption retrieval BibRef

Naghizadeh, A.[Alireza], Abavisani, M.[Mohammadsajad], Metaxas, D.N.[Dimitris N.],
Greedy AutoAugment,
PRL(138), 2020, pp. 624-630.
Elsevier DOI 2010
AutoAugment, Augmentation, ANN, Neural networks, Vision, Classification BibRef

Hu, C.[Cong], Wu, X.J.[Xiao-Jun], Li, Z.Y.[Zuo-Yong],
Generating adversarial examples with elastic-net regularized boundary equilibrium generative adversarial network,
PRL(140), 2020, pp. 281-287.
Elsevier DOI 2012
Adversarial example, Elastic-net regularization, Generative adversarial network, Semi-whitebox attack, Blackbox attack BibRef

Cen, F.[Feng], Zhao, X.Y.[Xiao-Yu], Li, W.[Wuzhuang], Wang, G.H.[Guang-Hui],
Deep feature augmentation for occluded image classification,
PR(111), 2021, pp. 107737.
Elsevier DOI 2012
Deep feature augmentation, Image occlusion, Convolutional neural networks, Image classification BibRef

Zhang, X., Wang, Z., Liu, D., Lin, Q., Ling, Q.,
Deep Adversarial Data Augmentation for Extremely Low Data Regimes,
CirSysVideo(31), No. 1, January 2021, pp. 15-28.
IEEE DOI 2101
Training, Generative adversarial networks, Data models, Task analysis, Semisupervised learning, object detection BibRef

Tran, N.T., Tran, V.H., Nguyen, N.B., Nguyen, T.K., Cheung, N.M.,
On Data Augmentation for GAN Training,
IP(30), 2021, pp. 1882-1897.
IEEE DOI 2101
Generative adversarial networks, Generators, Training, Task analysis, Standards, Data models, CycleGAN BibRef

Adamiak, M.[Maciej], Bedkowski, K.[Krzysztof], Majchrowska, A.[Anna],
Aerial Imagery Feature Engineering Using Bidirectional Generative Adversarial Networks: A Case Study of the Pilica River Region, Poland,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Wang, W.N.[Wen-Ning], Liu, X.B.[Xue-Bin], Mou, X.Q.[Xuan-Qin],
Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Liu, E.[Erhu], Huang, S.[Song], Zong, C.[Cheng], Zheng, C.Y.[Chang-You], Yao, Y.M.[Yong-Ming], Zhu, J.[Jing], Tang, S.Q.[Shi-Qi], Wang, Y.Q.[Yan-Qiu],
MTGAN: Extending Test Case set for Deep Learning Image Classifier,
IEICE(E104-D), No. 5, May 2021, pp. 709-722.
WWW Link. 2105
BibRef

Dvornik, N.[Nikita], Mairal, J.[Julien], Schmid, C.[Cordelia],
On the Importance of Visual Context for Data Augmentation in Scene Understanding,
PAMI(43), No. 6, June 2021, pp. 2014-2028.
IEEE DOI 2106
BibRef
Earlier:
Modeling Visual Context Is Key to Augmenting Object Detection Datasets,
ECCV18(XII: 375-391).
Springer DOI 1810
Context modeling, Object detection, Image segmentation, Semantics, Training, Visualization, Task analysis, semantic segmentation BibRef

Dvornik, N.[Nikita], Shmelkov, K., Mairal, J.[Julien], Schmid, C.[Cordelia],
BlitzNet: A Real-Time Deep Network for Scene Understanding,
ICCV17(4174-4182)
IEEE DOI 1802
image segmentation, learning (artificial intelligence), object detection, Semantics BibRef

Illarionova, S.[Svetlana], Nesteruk, S.[Sergey], Shadrin, D.[Dmitrii], Igantiev, V.[Vladimir], Pukalchik, M.[Maria], Oseledets, I.[Ivan],
MixChannel: Advanced Augmentation for Multispectral Satellite Images,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
Augmentation for training. BibRef

Xie, H.[Hao], Chen, Y.[Yushi], Ghamisi, P.[Pedram],
Remote Sensing Image Scene Classification via Label Augmentation and Intra-Class Constraint,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Peng, D.[Duo], Lei, Y.[Yinjie], Liu, L.Q.[Ling-Qiao], Zhang, P.P.[Ping-Ping], Liu, J.[Jun],
Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation,
IP(30), 2021, pp. 6594-6608.
IEEE DOI 2108
To train on synthetic data. Painting, Training, Semantics, Image segmentation, Complexity theory, Adaptation models, Task analysis, consistency regularization BibRef

Wang, H.[Hao], Wang, Q.[Qilong], Zhang, H.Z.[Hong-Zhi], Yang, J.[Jian], Zuo, W.M.[Wang-Meng],
Constrained Online Cut-Paste for Object Detection,
CirSysVideo(31), No. 10, October 2021, pp. 4071-4083.
IEEE DOI 2110
Augment samples by pasting foreground objects on other backgrounds. Switches, Training, Object detection, Detectors, Convergence, Coherence, Visualization, Object detection, data augmentation, sample diversity BibRef


Hao, W.[Weituo], El-Khamy, M.[Mostafa], Lee, J.[Jungwon], Zhang, J.Y.[Jian-Yi], Liang, K.J.[Kevin J], Chen, C.Y.[Chang-You], Carin, L.[Lawrence],
Towards Fair Federated Learning with Zero-Shot Data Augmentation,
TCV21(3305-3314)
IEEE DOI 2109
Computer aided instruction, Distance learning, Distributed databases, Collaborative work, Data models BibRef

Maeda, T.[Takahiro], Ukita, N.[Norimichi],
Data Augmentation for Human Motion Prediction,
MVA21(1-5)
DOI Link 2109
Training, Data acquisition, Humanoid robots, Kinematics, Animation, Data models BibRef

Yang, Y.[Yandan], Sheng, L.[Lu], Jiang, X.L.[Xiao-Long], Wang, H.[Haochen], Xu, D.[Dong], Cao, X.B.[Xian-Bin],
IncreACO: Incrementally Learned Automatic Check-out with Photorealistic Exemplar Augmentation,
WACV21(626-634)
IEEE DOI 2106
Training, Computational modeling, Layout, Pipelines, Data models BibRef

Uricár, M.[Michal], Sistu, G.[Ganesh], Rashed, H.[Hazem], Vobecký, A.[Antonín], Kumar, V.R.[Varun Ravi], Krížek, P.[Pavel], Bürger, F.[Fabian], Yogamani, S.[Senthil],
Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving,
WACV21(766-775)
IEEE DOI 2106
Degradation, Image segmentation, Semantics, Pipelines, Cameras, Generative adversarial networks, Task analysis BibRef

Olsson, V.[Viktor], Tranheden, W.[Wilhelm], Pinto, J.[Juliano], Svensson, L.[Lennart],
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning,
WACV21(1368-1377)
IEEE DOI 2106
Training, Image segmentation, Semantics, Manuals, Semisupervised learning BibRef

Mounsaveng, S.[Saypraseuth], Laradji, I.[Issam], Ben Ayed, I.[Ismail], Vázquez, D.[David], Pedersoli, M.[Marco],
Learning Data Augmentation with Online Bilevel Optimization for Image Classification,
WACV21(1690-1699)
IEEE DOI 2106
Training, Learning systems, Computational modeling, Machine learning, Data models BibRef

Inoue, N.[Nakamasa], Yamagata, E.[Eisuke], Kataoka, H.[Hirokatsu],
Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data,
ICPR21(1023-1028)
IEEE DOI 2105
Training, Knowledge engineering, Complexity theory, Colored noise, Optimization, Image classification BibRef

Suri, B.S.H.[Bhasker Sri Harsha], Yeturu, K.[Kalidas],
Pseudo Rehearsal using non photo-realistic images,
ICPR21(4797-4804)
IEEE DOI 2105
Neural networks, Memory management, Training data, Task analysis, Image classification BibRef

Hegde, G.[Guruprasad], Ramesh, A.N.[Avinash Nittur], Gandikota, K.V.[Kanchana Vaishnavi], Obermaisser, R.[Roman], Moeller, M.[Michael],
A Simple Domain Shifting Network for Generating Low Quality Images,
ICPR21(3963-3968)
IEEE DOI 2105
Image quality, Deep learning, Image recognition, Robot vision systems, Training data, Cameras, Pattern recognition BibRef

Patel, K.[Kanil], Beluch, W.[William], Zhang, D.[Dan], Pfeiffer, M.[Michael], Yang, B.[Bin],
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration,
ICPR21(8029-8036)
IEEE DOI 2105
Manifolds, Training, Uncertainty, Temperature, Estimation, Stochastic processes, Network architecture BibRef

Yang, H.[Hao], Zhou, Y.[Yun],
IDA-GAN: A Novel Imbalanced Data Augmentation GAN,
ICPR21(8299-8305)
IEEE DOI 2105
Training, Measurement, Network intrusion detection, Benchmark testing, Tools, Generative adversarial networks, GAN BibRef

Ma, W.X.[Wen-Xin], Chen, J.[Jian], Du, Q.[Qing], Jia, W.[Wei],
PointDrop: Improving Object Detection from Sparse Point Clouds via Adversarial Data Augmentation,
ICPR21(10004-10009)
IEEE DOI 2105
Training, Reflectivity, Solid modeling, Detectors, Object detection, Robustness, 3D object detection, adversarial learning BibRef

Sasaki, H.[Hiroshi], Willcocks, C.G.[Chris G.], Breckon, T.P.[Toby P.],
Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery,
ICPR21(5083-5090)
IEEE DOI 2105
Night vision, Interpolation, Surveillance, Focusing, Object detection, Pattern recognition, Security BibRef

Hung, S.K.[Shih-Kai], Gan, J.Q.[John Q.],
Augmentation of Small Training Data Using GANs for Enhancing the Performance of Image Classification,
ICPR21(3350-3356)
IEEE DOI 2105
Training, Neural networks, Training data, Machine learning, Generative adversarial networks, Robustness, Pattern recognition, GANs BibRef

Iwana, B.K.[Brian Kenji], Uchida, S.[Seiichi],
Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher,
ICPR21(3558-3565)
IEEE DOI 2105
Recurrent neural networks, Shape, Time series analysis, Tools, Data models, Convolutional neural networks, Pattern matching BibRef

Heindl, C.[Christoph], Brunner, L.[Lukas], Zambal, S.[Sebastian], Scharinger, J.[Josef],
Blendtorch: A Real-time, Adaptive Domain Randomization Library,
IML20(538-551).
Springer DOI 2103
To create synthetic training data. BibRef

Arantes, R.B.[Renato B.], Vogiatzis, G.[George], Faria, D.R.[Diego R.],
CSC-GAN: Cycle and Semantic Consistency for Dataset Augmentation,
ISVC20(I:170-181).
Springer DOI 2103
BibRef

Kataoka, H.[Hirokatsu], Okayasu, K.[Kazushige], Matsumoto, A.[Asato], Yamagata, E.[Eisuke], Yamada, R.[Ryosuke], Inoue, N.[Nakamasa], Nakamura, A.[Akio], Satoh, Y.[Yutaka],
Pre-training Without Natural Images,
ACCV20(VI:583-600).
Springer DOI 2103
BibRef

He, Z.W.[Ze-Wen], Wu, R.[Rui], Zhang, D.Q.[Ding-Qian],
Cog: Consistent Data Augmentation for Object Perception,
ACCV20(III:143-154).
Springer DOI 2103
BibRef

Kozerawski, J.[Jedrzej], Fragoso, V.[Victor], Karianakis, N.[Nikolaos], Mittal, G.[Gaurav], Turk, M.[Matthew], Chen, M.[Mei],
BLT: Balancing Long-tailed Datasets with Adversarially-perturbed Images,
ACCV20(III:338-355).
Springer DOI 2103
BibRef

Ammar, S.[Sirine], Bouwmans, T.[Thierry], Zaghden, N.[Nizar], Neji, M.[Mahmoud],
Towards an Effective Approach for Face Recognition with DCGANS Data Augmentation,
ISVC20(I:463-475).
Springer DOI 2103
BibRef

Le, H., Nguyen, M., Yan, W.Q.,
Machine Learning with Synthetic Data: A New Way to Learn and Classify the Pictorial Augmented Reality Markers in Real-Time,
IVCNZ20(1-6)
IEEE DOI 2012
Training, Visualization, Video sequences, Machine learning, Real-time systems, Task analysis, Augmented reality, synthetic data generated BibRef

Lou, L., Zhang, S., Zhang, S.,
Object Detection with the High-frequency Change of Objects Classes,
ISPRS20(B3:125-130).
DOI Link 2012
BibRef

Kuo, C.W.[Chia-Wen], Ma, C.Y.[Chih-Yao], Huang, J.B.[Jia-Bin], Kira, Z.[Zsolt],
Featmatch: Feature-based Augmentation for Semi-supervised Learning,
ECCV20(XVIII:479-495).
Springer DOI 2012
BibRef

Chen, Y.[Yunlu], Hu, V.T.[Vincent Tao], Gavves, E.[Efstratios], Mensink, T.[Thomas], Mettes, P.[Pascal], Yang, P.W.[Peng-Wan], Snoek, C.G.M.[Cees G. M.],
PointMixup: Augmentation for Point Clouds,
ECCV20(III:330-345).
Springer DOI 2012
interpolation between examples. BibRef

Hataya, R.[Ryuichiro], Zdenek, J.[Jan], Yoshizoe, K.[Kazuki], Nakayama, H.[Hideki],
Faster Autoaugment: Learning Augmentation Strategies Using Backpropagation,
ECCV20(XXV:1-16).
Springer DOI 2011
BibRef

Zou, J.H.[Jun-Hua], Pan, Z.S.[Zhi-Song], Qiu, J.Y.[Jun-Yang], Liu, X.[Xin], Rui, T.[Ting], Li, W.[Wei],
Improving the Transferability of Adversarial Examples with Resized-diverse-inputs, Diversity-ensemble and Region Fitting,
ECCV20(XXII:563-579).
Springer DOI 2011
BibRef

Yu, N.[Ning], Li, K.[Ke], Zhou, P.[Peng], Malik, J.[Jitendra], Davis, L.S.[Larry S.], Fritz, M.[Mario],
Inclusive GAN: Improving Data and Minority Coverage in Generative Models,
ECCV20(XXII:377-393).
Springer DOI 2011
BibRef

Li, Y.G.[Yong-Gang], Hu, G.S.[Guo-Sheng], Wang, Y.T.[Yong-Tao], Hospedales, T.M.[Timothy M.], Robertson, N.M.[Neil M.], Yang, Y.X.[Yong-Xin],
Differentiable Automatic Data Augmentation,
ECCV20(XXII:580-595).
Springer DOI 2011
BibRef

Zoph, B.[Barret], Cubuk, E.D.[Ekin D.], Ghiasi, G.[Golnaz], Lin, T.Y.[Tsung-Yi], Shlens, J.[Jonathon], Le, Q.V.[Quoc V.],
Learning Data Augmentation Strategies for Object Detection,
ECCV20(XXVII:566-583).
Springer DOI 2011
BibRef

Novozámský, A., Vít, D., Šroubek, F., Franc, J., Krbálek, M., Bílkova, Z., Zitová, B.,
Automated Object Labeling for CNN-Based Image Segmentation,
ICIP20(2036-2040)
IEEE DOI 2011
Generating training data for CNN training. Image segmentation, Training, Labeling, Automobiles, Task analysis, Coolants, Training data, CNN, SURF, U-net, automated object labeling, image segmentation BibRef

Zhang, K., Cao, Z., Wu, J.,
Circular Shift: An Effective Data Augmentation Method For Convolutional Neural Network On Image Classification,
ICIP20(1676-1680)
IEEE DOI 2011
Training, Agriculture, Task analysis, Data visualization, Image resolution, Neural networks, Data models, circular shift, convolutional neural network BibRef

Dionelis, N.[Nikolaos], Yaghoobi, M.[Mehrdad], Tsaftaris, S.A.[Sotirios A.],
Boundary Of Distribution Support Generator (BDSG): Sample Generation On The Boundary,
ICIP20(803-807)
IEEE DOI 2011
Anomaly detection, Generators, Data models, Convergence, Estimation, Computational modeling, Anomaly detection, invertible models BibRef

Ofori-Oduro, M., Amer, M.A.,
Data Augmentation Using Artificial Immune Systems For Noise-Robust CNN Models,
ICIP20(833-837)
IEEE DOI 2011
Training, Artificial intelligence, Data models, Object detection, White noise, Immune system, Cloning, Artificial Immune Systems, AIS, CNN BibRef

Tong, Y.H.[Yun-He], Wang, A.[Anjie], Tan, S.C.[Song-Chao], Wang, S.S.[Shan-She], Ma, S.W.[Si-Wei], Gao, W.[Wen],
Self-Supervised Learning of Depth and Pose Using Cycle Generative Adversarial Network,
ICIP20(738-742)
IEEE DOI 2011
Indexes, Self-supervised learning, CycleGAN, depth estimation, pose estimation, monocular BibRef

Tang, Z.Q.[Zhi-Qiang], Gao, Y.H.[Yun-He], Karlinsky, L.[Leonid], Sattigeri, P.[Prasanna], Feris, R.[Rogerio], Metaxas, D.[Dimitris],
Onlineaugment: Online Data Augmentation with Less Domain Knowledge,
ECCV20(VII:313-329).
Springer DOI 2011
BibRef

Ryoo, M.S.[Michael S.], Piergiovanni, A.J., Kangaspunta, J.[Juhana], Angelova, A.[Anelia],
Assemblenet++: Assembling Modality Representations via Attention Connections,
ECCV20(XX:654-671).
Springer DOI 2011
BibRef

Wang, X.F.[Xiao-Fang], Xiong, X.[Xuehan], Neumann, M.[Maxim], Piergiovanni, A.J., Ryoo, M.S.[Michael S.], Angelova, A.[Anelia], Kitani, K.M.[Kris M.], Hua, W.[Wei],
Attentionnas: Spatiotemporal Attention Cell Search for Video Classification,
ECCV20(VIII:449-465).
Springer DOI 2011
BibRef

Behl, H.S.[Harkirat Singh], Baydin, A.G.[Atilim Günes], Gal, R.[Ran], Torr, P.H.S.[Philip H. S.], Vineet, V.[Vibhav],
Autosimulate: (quickly) Learning Synthetic Data Generation,
ECCV20(XXII:255-271).
Springer DOI 2011
BibRef

Biffi, C.[Carlo], McDonagh, S.[Steven], Torr, P.H.S.[Philip H.S.], Leonardis, A.[Aleš], Parisot, S.[Sarah],
Many-shot from Low-shot: Learning to Annotate Using Mixed Supervision for Object Detection,
ECCV20(VIII:35-50).
Springer DOI 2011
Generate large number of annotations from a larger set of weakly labelled images. BibRef

Shetty, R.[Rakshith], Fritz, M.[Mario], Schiele, B.[Bernt],
Towards Automated Testing and Robustification by Semantic Adversarial Data Generation,
ECCV20(II:489-506).
Springer DOI 2011
BibRef

Olut, S.[Sahin], Shen, Z.Y.[Zheng-Yang], Xu, Z.[Zhenlin], Gerber, S.[Samuel], Niethammer, M.[Marc],
Adversarial Data Augmentation via Deformation Statistics,
ECCV20(XXIX: 643-659).
Springer DOI 2010
BibRef

Chu, P.[Peng], Bian, X.[Xiao], Liu, S.[Shaopeng], Ling, H.B.[Hai-Bin],
Feature Space Augmentation for Long-tailed Data,
ECCV20(XXIX: 694-710).
Springer DOI 2010
BibRef

Bernal, E.A.[Edgar A.],
Training Deep Generative Models in Highly Incomplete Data Scenarios with Prior Regularization,
LLID21(2631-2641)
IEEE DOI 2109
Training, Computational modeling, Statistical distributions, Data models, Filling BibRef

Richardson, T.W.[Trevor W.], Wu, W.C.[Wen-Cheng], Lin, L.[Lei], Xu, B.[Beilei], Bernal, E.A.[Edgar A.],
McFlow: Monte Carlo Flow Models for Data Imputation,
CVPR20(14193-14202)
IEEE DOI 2008
Issues of missing data. Data models, Task analysis, Monte Carlo methods, Computational modeling, Training, Training data BibRef

Koutilya, P.N.V.R., Zhou, H.[Hao], Jacobs, D.[David],
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation,
CVPR20(13971-13980)
IEEE DOI 2008
combining synthetic and real images when training. Task analysis, Estimation, Geometry, Training, Semantics, Image reconstruction, Computer vision BibRef

Hoffer, E., Ben-Nun, T., Hubara, I., Giladi, N., Hoefler, T., Soudry, D.,
Augment Your Batch: Improving Generalization Through Instance Repetition,
CVPR20(8126-8135)
IEEE DOI 2008
Training, Neural networks, Convergence, Correlation, Schedules, Standards, Task analysis BibRef

Jaipuria, N., Zhang, X., Bhasin, R., Arafa, M., Chakravarty, P., Shrivastava, S., Manglani, S., Murali, V.N.,
Deflating Dataset Bias Using Synthetic Data Augmentation,
DeepVision20(3344-3353)
IEEE DOI 2008
Task analysis, Data models, Training, Computer vision, Estimation, Autonomous vehicles BibRef

Kushwaha, A., Gupta, S., Bhanushali, A., Dastidar, T.R.,
Rapid Training Data Creation by Synthesizing Medical Images for Classification and Localization,
Microscopy20(4272-4279)
IEEE DOI 2008
Training, Biomedical imaging, Solid modeling, Data models, Microscopy, Object detection, Machine learning BibRef

Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.,
Randaugment: Practical automated data augmentation with a reduced search space,
EDLCV20(3008-3017)
IEEE DOI 2008
Task analysis, Distortion, Data models, Training, Noise measurement, Market research, Computational modeling BibRef

Luo, C., Zhu, Y., Jin, L., Wang, Y.,
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition,
CVPR20(13743-13752)
IEEE DOI 2008
Text recognition, Training, Writing, Shape, Robustness, Optimization, Task analysis BibRef

Smith, P., Ricanek, K.,
Mitigating Algorithmic Bias: Evolving an Augmentation Policy that is Non-Biasing,
WACVWS20(90-97)
IEEE DOI 2006
Face, Data models, Training, Neural networks, Encyclopedias, Electronic publishing BibRef

Mordido, G., Yang, H., Meinel, C.,
microbatchGAN: Stimulating Diversity with Multi-Adversarial Discrimination,
WACV20(3050-3059)
IEEE DOI 2006
Training, Task analysis, Generators, Games, Data models, Focusing BibRef

Kar, A., Prakash, A., Liu, M., Cameracci, E., Yuan, J., Rusiniak, M., Acuna, D., Torralba, A., Fidler, S.,
Meta-Sim: Learning to Generate Synthetic Datasets,
ICCV19(4550-4559)
IEEE DOI 2004
grammars, graph theory, image processing, learning (artificial intelligence), neural nets, probability, Engines BibRef

Bello, I., Zoph, B., Le, Q., Vaswani, A., Shlens, J.,
Attention Augmented Convolutional Networks,
ICCV19(3285-3294)
IEEE DOI 2004
convolutional neural nets, image classification, learning (artificial intelligence), object detection, Encoding BibRef

Tang, Z.Q.[Zhi-Qiang], Peng, X.[Xi], Li, T.F.[Ting-Feng], Zhu, Y.[Yizhe], Metaxas, D.[Dimitris],
AdaTransform: Adaptive Data Transformation,
ICCV19(2998-3006)
IEEE DOI 2004
learning (artificial intelligence), neural nets, high computational cost, leverage data transformation, Transforms BibRef

Noguchi, A., Harada, T.,
Image Generation From Small Datasets via Batch Statistics Adaptation,
ICCV19(2750-2758)
IEEE DOI 2004
image processing, neural nets, statistical analysis, image generation, small datasets, batch statistics adaptation, Convolution BibRef

Chen, W., Tian, L., Fan, L., Wang, Y.,
Augmentation Invariant Training,
CEFRL19(2963-2971)
IEEE DOI 2004
learning (artificial intelligence), neural net architecture, neural nets, insufficient generalization ability, Neural Network BibRef

Lin, C., Guo, M., Li, C., Yuan, X., Wu, W., Yan, J., Lin, D., Ouyang, W.,
Online Hyper-Parameter Learning for Auto-Augmentation Strategy,
ICCV19(6578-6587)
IEEE DOI 2004
computer vision, Gaussian processes, learning (artificial intelligence), optimisation, probability, Data models BibRef

Hinterstoisser, S.[Stefan], Pauly, O.[Olivier], Heibel, H.[Hauke], Martina, M.[Marek], Bokeloh, M.[Martin],
An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Detection,
R6D19(2787-2796)
IEEE DOI 2004
Create synthetic data for training. image texture, learning (artificial intelligence), neural nets, object detection, realistic images, Deep Learning BibRef

Fong, R.,
Occlusions for Effective Data Augmentation in Image Classification,
VXAI19(4158-4166)
IEEE DOI 2004
computer vision, image classification, neural nets, occlusions, data augmentation, image classification, deep networks, deep-learning BibRef

Carlucci, F.M., Russo, P., Tommasi, T., Caputo, B.,
Hallucinating Agnostic Images to Generalize Across Domains,
TASKCV19(3227-3234)
IEEE DOI 2004
image classification, learning (artificial intelligence), adversarial domain classifier, unlabeled target samples, multisource domain adaptation BibRef

Hong, S.E.[Sung-Eun], Kang, S.I.[Sung-Il], Cho, D.H.[Dong-Hyeon],
Patch-Level Augmentation for Object Detection in Aerial Images,
VisDrone19(127-134)
IEEE DOI 2004
data mining, feature extraction, image classification, image representation, image segmentation, class imbalance BibRef

Yu, A.[Aron], Grauman, K.[Kristen],
Thinking Outside the Pool: Active Training Image Creation for Relative Attributes,
CVPR19(708-718).
IEEE DOI 2002
BibRef

Cubuk, E.D.[Ekin D.], Zoph, B.[Barret], Mane, D.[Dandelion], Vasudevan, V.[Vijay], Le, Q.V.[Quoc V.],
AutoAugment: Learning Augmentation Strategies From Data,
CVPR19(113-123).
IEEE DOI 2002
BibRef

Luo, Z.X.[Zi-Xin], Shen, T.[Tianwei], Zhou, L.[Lei], Zhang, J.R.[Jia-Rui], Yao, Y.[Yao], Li, S.[Shiwei], Fang, T.[Tian], Quan, L.[Long],
ContextDesc: Local Descriptor Augmentation With Cross-Modality Context,
CVPR19(2522-2531).
IEEE DOI 2002
BibRef

Alipourfard, T., Arefi, H.,
Virtual Training Sample Generation by Generative Adversarial Networks for Hyperspectral Images Classification,
SMPR19(63-69).
DOI Link 1912
BibRef

Hoffmann, D.T.[David T.], Tzionas, D.[Dimitrios], Black, M.J.[Michael J.], Tang, S.[Siyu],
Learning to Train with Synthetic Humans,
GCPR19(609-623).
Springer DOI 1911
BibRef

Chen, Z., Huang, Y., Wang, L.,
Augmented Visual-Semantic Embeddings for Image and Sentence Matching,
ICIP19(290-294)
IEEE DOI 1910
Generative Adversarial Networks, Image and Sentence Matching, Visual-Semantic Embeddings BibRef

Dou, Y.M.[Yi-Min], Yu, X.R.[Xiang-Ru], Li, J.P.[Jin-Ping],
Feature GANs: A Model for Data Enhancement and Sample Balance of Foreign Object Detection in High Voltage Transmission Lines,
CAIP19(II:568-580).
Springer DOI 1909
BibRef

Bongini, P.[Pietro], del Chiaro, R.[Riccardo], Bagdanov, A.D.[Andrew D.], del Bimbo, A.[Alberto],
GADA: Generative Adversarial Data Augmentation for Image Quality Assessment,
CIAP19(II:214-224).
Springer DOI 1909
BibRef

Aguilar, E.[Eduardo], Radeva, P.[Petia],
Class-Conditional Data Augmentation Applied to Image Classification,
CAIP19(II:182-192).
Springer DOI 1909
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.M.[Nuno M.],
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:Oct 20, 2021 at 09:45:26