Fetching the paper…
Reading the bibliography…
In this paper, we propose a new method to perform data augmentation in a reliable way in the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based variational autoencoder.
M. A. Tanner and W. H. Wong, “The calculation of posterior distributions by data augmentation,” Journal of the American statistical Association , vol. 82, no. 398, pp. 528–540, 1987
1987
Earlier work this paper cites.
S. Duane, A. D. Kennedy, B. J. Pendleton, and D. Roweth, “Hybrid monte carlo,” Physics Letters B , vol. 195, no. 2, pp. 216–222, 1987
1987
Earlier work this paper cites.
Y. LeCun, “The MNIST database of handwritten digits,” 1998
1998
Earlier work this paper cites.
M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul, “An introduction to variational methods for graphical models,” Machine Learning , vol. 37, no. 2, pp. 183–233, 1999
1999
Earlier work this paper cites.
L. Breiman, “Random forests,” Machine Learning , vol. 45, no. 1, pp. 5–32, 2001
2001
Earlier work this paper cites.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of artificial intelligence research , vol. 16, pp. 321–357, 2002
2002
Earlier work this paper cites.
B. Leimkuhler and S. Reich, Simulating hamiltonian dynamics . Cambridge university press, 2004, vol. 14
2004
Earlier work this paper cites.
H. Han, W.-Y. Wang, and B.-H. Mao, “Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning,” in Advances in Intelligent Computing , D.-S. Huang, X.-P. Zhang, and G.-B. Huang, Eds. Springer Berlin Heidelberg, 2005, vol. 3644, pp. 878–887, series Title: LNCS
2005
Earlier work this paper cites.
R. M. Neal, “Hamiltonian importance sampling,” in talk presented at the Banff International Research Station (BIRS) workshop on Mathematical Issues in Molecular Dynamics , 2005
2005
Earlier work this paper cites.
E. Hairer, C. Lubich, and G. Wanner, Geometric numerical integration: structure-preserving algorithms for ordinary differential equations . Springer Science & Business Media, 2006, vol. 31
2006
Earlier work this paper cites.
G. Lebanon, “Metric learning for text documents,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 28, no. 4, pp. 497–508, 2006
2006
Earlier work this paper cites.
S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, “Supervised machine learning: A review of classification techniques,” Emerging artificial intelligence applications in computer engineering , vol. 160, no. 1, pp. 3–24, 2007
2007
Earlier work this paper cites.
Haibo He, Yang Bai, E. A. Garcia, and Shutao Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” in 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) . IEEE, 2008, pp. 1322–1328
2008
Earlier work this paper cites.
J. S. Liu, Monte Carlo strategies in scientific computing . Springer Science & Business Media, 2008
2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
K. A. Ellis, A. I. Bush, D. Darby, D. De Fazio, J. Foster, P. Hudson, N. T. Lautenschlager, N. Lenzo, R. N. Martins, P. Maruff, C. Masters, A. Milner, K. Pike, C. Rowe, G. Savage, C. Szoeke, K. Taddei, V. Villemagne, M. Woodward, D. Ames, and AIBL Research Group, “The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease,” International Psychogeriatrics , vol. 21, no. 4, pp. 672–687, 2009
2009
Earlier work this paper cites.
V. Fonov, A. Evans, R. McKinstry, C. Almli, and D. Collins, “Unbiased nonlinear average age-appropriate brain templates from birth to adulthood,” NeuroImage , vol. 47, p. S102, 2009
2009
Earlier work this paper cites.
N. J. Tustison, B. B. Avants, P. A. Cook, Yuanjie Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee, “N4ITK: Improved N3 Bias Correction,” IEEE Transactions on Medical Imaging , vol. 29, no. 6, pp. 1310–1320, 2010
2010
Earlier work this paper cites.
H. M. Nguyen, E. W. Cooper, and K. Kamei, “Borderline over-sampling for imbalanced data classification,” International Journal of Knowledge Engineering and Soft Data Paradigms , vol. 3, no. 1, pp. 4–21, 2011
2011
Earlier work this paper cites.
R. M. Neal and others, “MCMC using hamiltonian dynamics,” Handbook of Markov Chain Monte Carlo , vol. 2, no. 11, p. 2, 2011
2011
Earlier work this paper cites.
M. Girolami and B. Calderhead, “Riemann manifold langevin and hamiltonian monte carlo methods,” Journal of the Royal Statistical Society: Series B (Statistical Methodology) , vol. 73, no. 2, pp. 123–214, 2011
2011
Earlier work this paper cites.
V. Fonov, A. C. Evans, K. Botteron, C. R. Almli, R. C. McKinstry, and D. L. Collins, “Unbiased average age-appropriate atlases for pediatric studies,” NeuroImage , vol. 54, no. 1, pp. 313–327, 2011
2011
Earlier work this paper cites.
S. Barua, M. M. Islam, X. Yao, and K. Murase, “MWMOTE–majority weighted minority oversampling technique for imbalanced data set learning,” IEEE Transactions on Knowledge and Data Engineering , vol. 26, no. 2, pp. 405–425, 2012
2012
Earlier work this paper cites.
J. Bergstra and Y. Bengio, “Random Search for Hyper-Parameter Optimization,” Journal of Machine Learning Research , vol. 13, no. Feb, pp. 281–305, 2012
2012
Earlier work this paper cites.
K. S. Button, J. P. Ioannidis, C. Mokrysz, B. A. Nosek, J. Flint, E. S. Robinson, and M. R. Munafò, “Power failure: why small sample size undermines the reliability of neuroscience,” Nature Reviews Neuroscience , vol. 14, no. 5, pp. 365–376, 2013
2013
Earlier work this paper cites.
R. Blagus and L. Lusa, “SMOTE for high-dimensional class-imbalanced data,” BMC Bioinformatics , vol. 14, no. 1, p. 106, 2013
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv:1312.6114 [cs, stat] , 2014
2014
Earlier work this paper cites.
D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” in International conference on machine learning . PMLR, 2014, pp. 1278–1286
2014
Earlier work this paper cites.
B. B. Avants, N. J. Tustison, M. Stauffer, G. Song, B. Wu, and J. C. Gee, “The Insight ToolKit image registration framework,” Frontiers in Neuroinformatics , vol. 8, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
T. Salimans, D. Kingma, and M. Welling, “Markov chain monte carlo and variational inference: Bridging the gap,” in International Conference on Machine Learning , 2015, pp. 1218–1226
2015
Earlier work this paper cites.
D. Rezende and S. Mohamed, “Variational inference with normalizing flows,” in International Conference on Machine Learning . PMLR, 2015, pp. 1530–1538
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,” in 2015 IEEE International Conference on Computer Vision (ICCV) . Santiago, Chile: IEEE, 2015, pp. 1026–1034
2015
Earlier work this paper cites.
I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, Deep learning . MIT press Cambridge, 2016, vol. 1, issue: 2
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. D. Hoffman and M. J. Johnson, “Elbo surgery: yet another way to carve up the variational evidence lower bound,” in Workshop in Advances in Approximate Bayesian Inference, NIPS , vol. 1, 2016, p. 2
2016
Earlier work this paper cites.
E. Nalisnick, L. Hertel, and P. Smyth, “Approximate inference for deep latent gaussian mixtures,” in NIPS Workshop on Bayesian Deep Learning , vol. 2, 2016, p. 131
2016
Earlier work this paper cites.
C. K. Sønderby, T. Raiko, L. Maaløe, S. K. Sønderby, and O. Winther, “Ladder variational autoencoder,” in 29th Annual Conference on Neural Information Processing Systems (NIPS 2016) , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
G. Arvanitidis, L. K. Hansen, and S. Hauberg, “A locally adaptive normal distribution,” Advances in Neural Information Processing Systems , pp. 4258–4266, 2016
2016
Cited alongside, same era.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” in Advances in Neural Information Processing Systems , 2016
2016
Cited alongside, same era.
K. J. Gorgolewski, T. Auer, V. D. Calhoun, R. C. Craddock, S. Das, E. P. Duff, G. Flandin, S. S. Ghosh, T. Glatard, Y. O. Halchenko, D. A. Handwerker, M. Hanke, D. Keator, X. Li, Z. Michael, C. Maumet, B. N. Nichols, T. E. Nichols, J. Pellman, J.-B. Poline, A. Rokem, G. Schaefer, V. Sochat, W. Triplett, J. A. Turner, G. Varoquaux, and R. A. Poldrack, “The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments,” Scientific Data , vol. 3, no. 1, p. 160044, 2016
2016
Cited alongside, same era.
2018
Later among the works it cites.
N. Chen, A. Klushyn, R. Kurle, X. Jiang, J. Bayer, and P. Smagt, “Metrics for deep generative models,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2018, pp. 1540–1550
2018
Later among the works it cites.
H. Shao, A. Kumar, and P. T. Fletcher, “The riemannian geometry of deep generative models,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, 2018, pp. 428–4288
2018
Later among the works it cites.
G. Arvanitidis, L. K. Hansen, and S. Hauberg, “Latent space oddity: On the curvature of deep generative models,” in 6th International Conference on Learning Representations, ICLR 2018 , 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
F. Calimeri, A. Marzullo, C. Stamile, and G. Terracina, “Biomedical data augmentation using generative adversarial neural networks,” in International conference on artificial neural networks . Springer, 2017, pp. 626–634
2017
Cited alongside, same era.
L. Bi, J. Kim, A. Kumar, D. Feng, and M. Fulham, “Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs),” in Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment , ser. LNCS. Springer, 2017, pp. 43–51
2017
Cited alongside, same era.
W.-N. Hsu, Y. Zhang, and J. Glass, “Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation,” in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2017, pp. 16–23
2017
Cited alongside, same era.
H. Nishizaki, “Data augmentation and feature extraction using variational autoencoder for acoustic modeling,” in 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) . IEEE, 2017, pp. 1222–1227
2017
Cited alongside, same era.
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner, “beta-VAE: Learning basic visual concepts with a constrained variational framework.” ICLR , vol. 2, no. 5, p. 6, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
G. Cohen, S. Afshar, J. Tapson, and A. Van Schaik, “Emnist: Extending mnist to handwritten letters,” in 2017 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2017, pp. 2921–2926
2017
Cited alongside, same era.
B. Dai and D. Wipf, “Diagnosing and enhancing vae models,” in International Conference on Learning Representations , 2018
2018
Later among the works it cites.
M. Lucic, K. Kurach, M. Michalski, S. Gelly, and O. Bousquet, “Are GANs created equal? a large-scale study,” in Advances in Neural Information Processing Systems , 2018, p. 10
2018
Later among the works it cites.
K. Shmelkov, C. Schmid, and K. Alahari, “How good is my gan?” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 213–229
2018
Later among the works it cites.
K. Aderghal, A. Khvostikov, A. Krylov, J. Benois-Pineau, K. Afdel, and G. Catheline, “Classification of Alzheimer Disease on Imaging Modalities with Deep CNNs Using Cross-Modal Transfer Learning,” in 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS) , 2018, pp. 345–350, iSSN: 2372-9198
2018
Later among the works it cites.
K. Bäckström, M. Nazari, I.-H. Gu, and A. Jakola, “An efficient 3D deep convolutional network for Alzheimer’s disease diagnosis using MR images,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) , vol. 2018-April, 2018, pp. 149–153
2018
Later among the works it cites.
V. S. Fonov, M. Dadar, T. P.-A. R. Group, and D. L. Collins, “Deep learning of quality control for stereotaxic registration of human brain MRI,” bioRxiv , p. 303487, 2018
2018
Later among the works it cites.
C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” Journal of Big Data , vol. 6, no. 1, p. 60, 2019
2019
Later among the works it cites.
X. Yi, E. Walia, and P. Babyn, “Generative adversarial network in medical imaging: A review,” Medical image analysis , vol. 58, p. 101552, 2019
2019
Later among the works it cites.
V. Sandfort, K. Yan, P. J. Pickhardt, and R. M. Summers, “Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks,” Scientific reports , vol. 9, no. 1, p. 16884, 2019
2019
Later among the works it cites.
Y. Liu, Y. Zhou, X. Liu, F. Dong, C. Wang, and Z. Wang, “Wasserstein gan-based small-sample augmentation for new-generation artificial intelligence: a case study of cancer-staging data in biology,” Engineering , vol. 5, no. 1, pp. 156–163, 2019
2019
Later among the works it cites.
Z. Wu, S. Wang, Y. Qian, and K. Yu, “Data augmentation using variational autoencoder for embedding based speaker verification,” in Interspeech 2019 . ISCA, 2019, pp. 1163–1167
2019
Later among the works it cites.
P. Zhuang, A. G. Schwing, and O. Koyejo, “fMRI data augmentation via synthesis,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) . IEEE, 2019, pp. 1783–1787
2019
Later among the works it cites.
N. Painchaud, Y. Skandarani, T. Judge, O. Bernard, A. Lalande, and P.-M. Jodoin, “Cardiac MRI segmentation with strong anatomical guarantees,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2019, pp. 632–640
2019
Later among the works it cites.
F. Ruiz and M. Titsias, “A contrastive divergence for combining variational inference and mcmc,” in International Conference on Machine Learning . PMLR, 2019, pp. 5537–5545
2019
Later among the works it cites.
A. Klushyn, N. Chen, R. Kurle, and B. Cseke, “Learning Hierarchical Priors in VAEs,” Advances in neural information processing systems , p. 10, 2019
2019
Later among the works it cites.
M. Bauer and A. Mnih, “Resampled priors for variational autoencoders,” in The 22nd International Conference on Artificial Intelligence and Statistics . PMLR, 2019, pp. 66–75
2019
Later among the works it cites.
E. Mathieu, C. Le Lan, C. J. Maddison, R. Tomioka, and Y. W. Teh, “Continuous hierarchical representations with poincaré variational auto-encoders,” in Advances in neural information processing systems , 2019, pp. 12 565–12 576
2019
Later among the works it cites.
2019
Later among the works it cites.
M. Louis, “Computational and statistical methods for trajectory analysis in a Riemannian geometry setting,” PhD Thesis, Sorbonnes universités, 2019
2019
Later among the works it cites.
A. Borji, “Pros and cons of GAN evaluation measures,” Computer Vision and Image Understanding , vol. 179, pp. 41–65, 2019
2019
Later among the works it cites.
K. Oh, Y.-C. Chung, K. W. Kim, W.-S. Kim, and I.-S. Oh, “Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning,” Scientific Reports , vol. 9, no. 1, p. 18150, 2019
2019
Later among the works it cites.
A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman, and P. R. Pinheiro, “Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection,” Ieee Access , vol. 8, pp. 91 916–91 923, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
A. Razavi, A. v. d. Oord, and O. Vinyals, “Generating diverse high-fidelity images with vq-vae-2,” Advances in Neural Information Processing Systems , 2020
2020
Later among the works it cites.
B. Pang, T. Han, E. Nijkamp, S.-C. Zhu, and Y. N. Wu, “Learning latent space energy-based prior model,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
P. Ghosh, M. S. Sajjadi, A. Vergari, M. Black, and B. Schölkopf, “From variational to deterministic autoencoders,” in 8th International Conference on Learning Representations, ICLR 2020 , 2020
2020
Later among the works it cites.
I. Ovinnikov, “Poincaré wasserstein autoencoder,” arXiv:1901.01427 [cs, stat] , 2020-03-16
2020
Later among the works it cites.
N. Miolane and S. Holmes, “Learning weighted submanifolds with variational autoencoders and riemannian variational autoencoders,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 14 503–14 511
2020
Later among the works it cites.
D. Kalatzis, D. Eklund, G. Arvanitidis, and S. Hauberg, “Variational autoencoders with riemannian brownian motion priors,” in International Conference on Machine Learning . PMLR, 2020, pp. 5053–5066
2020
Later among the works it cites.
2020
Later among the works it cites.
J. Wen, E. Thibeau-Sutre, M. Diaz-Melo, J. Samper-González, A. Routier, S. Bottani, D. Dormont, S. Durrleman, N. Burgos, and O. Colliot, “Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation,” Medical Image Analysis , vol. 63, p. 101694, 2020
2020
Later among the works it cites.
J. Islam and Y. Zhang, “GAN-based synthetic brain PET image generation,” Brain Informatics , vol. 7, no. 1, 2020
2020
Later among the works it cites.
M. Liu, J. Zhang, C. Lian, and D. Shen, “Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores,” IEEE Transactions on Cybernetics , vol. 50, no. 7, pp. 3381–3392, 2020
2020
Later among the works it cites.
A. Routier, N. Burgos, M. Díaz, M. Bacci, S. Bottani, O. El-Rifai, S. Fontanella, P. Gori, J. Guillon, A. Guyot, R. Hassanaly, T. Jacquemont, P. Lu, A. Marcoux, T. Moreau, J. Samper-González, M. Teichmann, E. Thibeau-Sutre, G. Vaillant, J. Wen, A. Wild, M.-O. Habert, S. Durrleman, and O. Colliot, “Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies,” Frontiers in Neuroinformatics , vol. 15, p. 689675, 2021
2021
Closest in time.
E. Thibeau-Sutre, M. Diaz, R. Hassanaly, A. M. Routier, D. Dormont, O. Colliot, and N. Burgos, “ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing,” 2021
2021
Closest in time.