Scattering transform



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A transform landing somewhere between wavelets and convnets which can encode some desirable invariances (translation, rotation), and multiple moments of a random field. More than that I do not know. the original authors do, though; (Bruna and Mallat 2013; Mallat 2012).

Cheng and Ménard (2021) summarises some recent research in scattering transforms .

References

Bruna, Joan, and Stephane Mallat. 2013. Invariant Scattering Convolution Networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (8): 1872–86.
———. 2019. Multiscale Sparse Microcanonical Models.” arXiv:1801.02013 [Math-Ph, Stat], May.
Bruna, Joan, Stéphane Mallat, Emmanuel Bacry, and Jean-François Muzy. 2015. Intermittent Process Analysis with Scattering Moments.” The Annals of Statistics 43 (1): 323–51.
Cheng, Sihao, and Brice Ménard. 2021. How to Quantify Fields or Textures? A Guide to the Scattering Transform.” arXiv:2112.01288 [Astro-Ph, Physics:physics], November.
Mallat, Stéphane. 2012. Group Invariant Scattering.” Communications on Pure and Applied Mathematics 65 (10): 1331–98.
Oyallon, Edouard, Eugene Belilovsky, and Sergey Zagoruyko. 2017. Scaling the Scattering Transform: Deep Hybrid Networks.” arXiv Preprint arXiv:1703.08961.
Sprechmann, Pablo, Joan Bruna, and Yann LeCun. 2014. Audio Source Separation with Discriminative Scattering Networks.” arXiv:1412.7022 [Cs], December.

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