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Despite substantial progress in signal source separation, results for richly structured data continue to contain perceptible artifacts.
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images
Geman, S. and Geman, D · 1984
Earlier work this paper cites.
Independent component analysis, a new concept?
Comon, P · 1994
Earlier work this paper cites.
An information-maximization approach to blind separation and blind deconvolution
Bell, A. J. and Sejnowski, T. J · 1995
Earlier work this paper cites.
Maximum likelihood blind source separation: A context-sensitive generalization of ica
Pearlmutter, B. A. and Parra, L. C · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Learning the parts of objects by non-negative matrix factorization
Lee, D. D. and Seung, H. S · 1999
Earlier work this paper cites.
Blind source separation of more sources than mixtures using overcomplete representations
Lee, T.-W., Lewicki, M. S., Girolami, M., and Sejnowski, T. J · 1999
Earlier work this paper cites.
Signal separation of background eeg and spike by using morphological filter
Nishida, S., Nakamura, M., Ikeda, A., and Shibasaki, H · 1999
Earlier work this paper cites.
Algorithms for non-negative matrix factorization
Lee, D. D. and Seung, H. S · 2001
Earlier work this paper cites.
One microphone source separation
Roweis, S. T · 2001
Earlier work this paper cites.
Colorization using optimization
Levin, A., Lischinski, D., and Weiss, Y · 2004
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
Earlier work this paper cites.
Audio source separation with a single sensor
Benaroya, L., Bimbot, F., and Gribonval, R · 2005
Earlier work this paper cites.
Single-channel speech separation using sparse non-negative matrix factorization
Schmidt, M. N. and Olsson, R. K · 2006
Earlier work this paper cites.
Source separation using single channel ica
Davies, M. E. and James, C. J · 2007
Earlier work this paper cites.
Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria
Virtanen, T · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Source-filter based clustering for monaural blind source separation
Spiertz, M. and Gnann, V · 2009
Earlier work this paper cites.
To explain or to predict?
Shmueli, G. et al · 2010
Earlier work this paper cites.
Mcmc using hamiltonian dynamics
Neal, R. M. et al · 2011
Cited alongside, same era.
Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
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Singing-voice separation from monaural recordings using robust principal component analysis
Huang, P.-S., Chen, S. D., Smaragdis, P., and Hasegawa-Johnson, M · 2012
Cited alongside, same era.
Singing-voice separation from monaural recordings using deep recurrent neural networks
Huang, P.-S., Kim, M., Hasegawa-Johnson, M., and Smaragdis, P · 2014
Cited alongside, same era.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
Cited alongside, same era.
Singing voice separation with deep u-net convolutional networks
Jansson, A., Humphrey, E., Montecchio, N., Bittner, R., Kumar, A., and Weyde, T · 2017
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Dynamic routing between capsules
Sabour, S., Frosst, N., and Hinton, G. E · 2017
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Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications
Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
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A neural network alternative to non-negative audio models
Smaragdis, P. and Venkataramani, S · 2017
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Neural discrete representation learning
van den Oord, A., Vinyals, O., et al · 2017
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Neural network alternatives toconvolutive audio models for source separation
Venkataramani, S., Subakan, C., and Smaragdis, P · 2017
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Texture synthesis using convolutional neural networks
Gatys, L., Ecker, A. S., and Bethge, M · 2015
Cited alongside, same era.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Cited alongside, same era.
Joint optimization of masks and deep recurrent neural networks for monaural source separation
Huang, P.-S., Kim, M., Hasegawa-Johnson, M., and Smaragdis, P · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
Cited alongside, same era.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015
Cited alongside, same era.
Image style transfer using convolutional neural networks
Gatys, L. A., Ecker, A. S., and Bethge, M · 2016
Cited alongside, same era.
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Slope meets lasso: improved oracle bounds and optimality
Bellec, P. C., Lecué, G., Tsybakov, A. B., et al · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Image transformer
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, Ł., Shazeer, N., Ku, A., and Tran, D · 2018
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Adversarial semi-supervised audio source separation applied to singing voice extraction
Stoller, D., Ewert, S., and Dixon, S · 2018
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Generative adversarial source separation
Subakan, Y. C. and Smaragdis, P · 2018
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Deep image prior
Ulyanov, D., Vedaldi, A., and Lempitsky, V · 2018
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Single image reflection separation with perceptual losses
Zhang, X., Ng, R., and Chen, Q · 2018
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Music source separation in the waveform domain
Défossez, A., Usunier, N., Bottou, L., and Bach, F · 2019
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Double-dip”: Unsupervised image decomposition via coupled deep-image-priors
Gandelsman, Y., Shocher, A., and Irani, M · 2019
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Neural separation of observed and unobserved distributions
Halperin, T., Ephrat, A., and Hoshen, Y · 2019
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Single-channel signal separation and deconvolution with generative adversarial networks
Kong, Q., Xu, Y., Jackson, P. J. B., Wang, W., and Plumbley, M. D · 2019
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End-to-end music source separation: is it possible in the waveform domain?
Lluis, F., Pons, J., and Serra, X · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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