Fetching the paper…
Reading the bibliography…
The ability of a classifier to recognize unknown inputs is important for many classification-based systems.
Estimating the support of a high-dimensional distribution
B. Schölkopf, J. C. Platt, J. C. Shawe-Taylor, A. J. Smola, and R. C. Williamson · 2001
Earlier work this paper cites.
Icdar 2003 robust reading competitions
S. M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong, and R. Young · 2003
Earlier work this paper cites.
Support vector data description
D. M. J. Tax and Robert P.W. Duin · 2004
Earlier work this paper cites.
Testing statistical hypotheses
E. L. Lehmann and J. P. Romano · 2005
Earlier work this paper cites.
A classification framework for anomaly detection
I. Steinwart, D. Hush, and C. Scovel · 2005
Earlier work this paper cites.
Anomaly detection: A survey
V. Chandola, A. Banerjee, and V Kumar · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Semi-supervised novelty detection
G. Blanchard, G. Lee, and C. Scott · 2010
Earlier work this paper cites.
notmnist dataset
Y. Bulatov · 2011
Earlier work this paper cites.
Libsvm: A library for support vector machines
C.-C. Chang and C.-J. Lin · 2011
Earlier work this paper cites.
Classification with asymmetric label noise: Consistency and maximal denoising
C. Scott, G Blanchard, and G. Handy · 2013
Cited alongside, same era.
An experimental evaluation of novelty detection methods
X. Ding, Y. Li, A. Belatreche, and L. P. Maguire · 2014
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Review: A review of novelty detection
M. A. F. Pimentel, D. A. Clifton, L. Clifton, and L. Tarassenko · 2014
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Semi-supervised learning with generative adversarial networks
A. Odena · 2016
Later among the works it cites.
Context encoders: Feature learning by inpainting
D. Pathak, P. Krähenbühl, J. Donahue, Y. Darrell, and A. A. Efros · 2016
Later among the works it cites.
Generative adversarial text to image synthesis
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H Lee · 2016
Later among the works it cites.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
T. Salimans and D. P Kingma · 2016
Later among the works it cites.
Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, and X. Chen · 2016
Later among the works it cites.
Theano: A Python framework for fast computation of mathematical expressions
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Radford, M. Luke, and S. Chintala · 2015
Cited alongside, same era.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
J. T. Springenberg · 2015
Cited alongside, same era.
Out-of-class novelty generation: an experimental foundation
M. Cherti, B. Kegl, and A. Kazakci · 2016
Cited alongside, same era.
Nips 2016 tutorial: Generative adversarial networks
I. Goodfellow · 2016
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
P. Isola, .J Zhu, T. Zhou, and A. A. Efros · 2016
Cited alongside, same era.
Theano Development Team · 2016
Later among the works it cites.
Generating videos with scene dynamics
C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
Later among the works it cites.
Good semi-supervised learning that requires a bad gan
Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. Salakhutdinov · 2017
Later among the works it cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2017
Later among the works it cites.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2017
Later among the works it cites.