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Wavelet transformation stands as a cornerstone in modern data analysis and signal processing.
Compression with flows via local bits-back coding
Ho, J., Lohn, E., and Abbeel, P · 1905
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Integer discrete flows and lossless compression
Hoogeboom, E., Peters, J. W. T., Berg, R. v. d., and Welling, M · 1905
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Zur theorie der orthogonalen funktionensysteme
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A mathematical theory of communication
Shannon, C. E · 1948
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A method for the construction of minimum-redundancy codes
Huffman, D. A · 1952
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Calculation of the amount of information about a random function contained in another such function
GEL’FAND, I. M · 1959
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Theory of edge detection
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A computational approach to edge detection
Canny, J · 1986
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Sub-band coding of digital images using symmetric short kernel filters and arithmetic coding techniques
Le Gall, D. and Tabatabai, A · 1988
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Wavelet shrinkage and wvd: a 10-minute tour
Donoho, D. L · 1992
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Singularity detection and processing with wavelets
Mallat, S. and Hwang, W. L · 1992
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Ideal spatial adaptation by wavelet shrinkage
Donoho, D. L. and Johnstone, J. M · 1994
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Wavelet-based volume morphing
He, T., Wang, S., and Kaufman, A · 1994
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Construction and applications of wavelets in numerical analysis
Sweldens, W · 1994
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Png (portable network graphics) specification version 1.0
Boutell, T. and Lane, T · 1997
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A new wavelet-based scheme for watermarking images
Dugad, R., Ratakonda, K., and Ahuja, N · 1998
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The lifting scheme: A construction of second generation wavelets
Sweldens, W · 1998
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Jpeg2000: A new standard for still image compression
Charrier, M · 1999
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Discrete-time signal processing
Oppenheim, A. V · 1999
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Using wavelets for solving pdes: an adaptive collocation method
Cruz, P., Mendes, A., and Magalhães, F. D · 2001
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Color transfer between images
Reinhard, E., Adhikhmin, M., Gooch, B., and Shirley, P · 2001
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Jpeg2000: Image compression fundamentals, standards and practice
Rabbani, M · 2002
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Jpeg file interchange format
Hamilton, E · 2004
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Bayesian image segmentation using wavelet-based priors
Figueiredo, M. A · 2005
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Idf++: Analyzing and improving integer discrete flows for lossless compression
Berg, R. v. d., Gritsenko, A. A., Dehghani, M., Sønderby, C. K., and Salimans, T · 2006
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A downsampled variant of imagenet as an alternative to the cifar datasets, 2017
Chrabaszcz, P., Loshchilov, I., and Hutter, F · 2017
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Wavelet-srnet: A wavelet-based cnn for multi-scale face super resolution
Huang, H., He, R., Sun, Z., and Tan, T · 2017
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Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
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A-nice-mc: Adversarial training for mcmc
Song, J., Zhao, S., and Ermon, S · 2017
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Spline filters for end-to-end deep learning
Balestriero, R., Cosentino, R., Glotin, H., and Baraniuk, R · 2018
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Duda, J · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Discrete wavelet transformations: An elementary approach with applications
Van Fleet, P. J · 2011
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Duda, J · 2013
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Empirical wavelet transform
Gilles, J · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Chen, R. T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D · 2018
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Learning filter widths of spectral decompositions with wavelets
Khan, H. and Yener, B · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Neural network renormalization group
Li, S.-H. and Wang, L · 2018
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Interpretable convolutional filters with sincnet
Ravanelli, M. and Bengio, Y · 2018
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Joint sub-bands learning with clique structures for wavelet domain super-resolution
Zhong, Z., Shen, T., Yang, Y., Lin, Z., and Zhang, C · 2018
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Guided image generation with conditional invertible neural networks
Ardizzone, L., Lüth, C., Kruse, J., Rother, C., and Köthe, U · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Noé, F., Olsson, S., Köhler, J., and Wu, H · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Learnable group transform for time-series
Cosentino, R. and Aazhang, B · 2020
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Machine learning holographic mapping by neural network renormalization group
Hu, H.-Y., Li, S.-H., Wang, L., and You, Y.-Z · 2020
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Neural canonical transformation with symplectic flows
Li, S.-H., Dong, C.-X., Zhang, L., and Wang, L · 2020
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Disentanglement by nonlinear ica with general incompressible-flow networks (gin), 2020
Sorrenson, P., Rother, C., and Köthe, U · 2020
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Wavelet flow: Fast training of high resolution normalizing flows
Yu, J., Derpanis, K., and Brubaker, M. A · 2020
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