A robust variational autoencoder using beta divergence

[1]  Ender Konukoglu,et al.  Unsupervised lesion detection via image restoration with a normative prior. , 2020, Medical image analysis.

[2]  John P. Cunningham,et al.  The continuous Bernoulli: fixing a pervasive error in variational autoencoders , 2019, NeurIPS.

[3]  Ender Konukoglu,et al.  Unsupervised Lesion Detection via Image Restoration with a Normative Prior , 2018, MIDL.

[4]  Nassir Navab,et al.  Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images , 2018, BrainLes@MICCAI.

[5]  Konstantinos Kamnitsas,et al.  Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders , 2018 .

[6]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[7]  Hao Zhang,et al.  Robust Variational Auto-Encoder for Radar HRRP Target Recognition , 2017, IScIDE.

[8]  Soonmee Cha,et al.  Current Clinical Brain Tumor Imaging , 2017, Neurosurgery.

[9]  Randy C. Paffenroth,et al.  Anomaly Detection with Robust Deep Autoencoders , 2017, KDD.

[10]  Matt J. Kusner,et al.  Grammar Variational Autoencoder , 2017, ICML.

[11]  Zhe Gan,et al.  Variational Autoencoder for Deep Learning of Images, Labels and Captions , 2016, NIPS.

[12]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[13]  Zhaohui Wu,et al.  Robust feature learning by stacked autoencoder with maximum correntropy criterion , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[15]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[16]  Andrzej Cichocki,et al.  Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities , 2010, Entropy.

[17]  Shogo Kato,et al.  Entropy and Divergence Associated with Power Function and the Statistical Application , 2010, Entropy.

[18]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[19]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[20]  M. C. Jones,et al.  Robust and efficient estimation by minimising a density power divergence , 1998 .

[21]  A. Basu,et al.  The trade-off between robustness and efficiency and the effect of model smoothing in minimum disparity inference , 1994 .

[22]  A. Zellner Optimal Information Processing and Bayes's Theorem , 1988 .

[23]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[24]  Gang Hua,et al.  Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models , 2018, J. Mach. Learn. Res..

[25]  et al.,et al.  ISLES 2015 ‐ A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI , 2017, Medical Image Anal..

[26]  Sungzoon Cho,et al.  Variational Autoencoder based Anomaly Detection using Reconstruction Probability , 2015 .

[27]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[28]  B. Kale,et al.  Maximum likelihood estimation in the presence of outiliers , 1988 .