From Scattering to Spectral Networks
Speaker: Joan Bruna, CIMS
Location: Warren Weaver Hall 1302
Date: May 2, 2014, 11:30 a.m.
Host: Denis Zorin
Object and Texture Classification are fundamental problems in which one is required to extract stable, discriminative information out of noisy, high-dimensional signals. Our perception of image and audio patterns is invariant under several transformations, such as illumination changes, translations or frequency transpositions, as well as small geometrical perturbations. Similarly, textures are examples of stationary, non-gaussian, intermittent processes which can be recognized from few realizations. Scattering operators construct a non-linear signal representation by cascading wavelet modulus decompositions, shown to be stable to geometric deformations, and capturing high-order moments with low-variance estimators. Moreover, scattering coefficients encode the presence of geometric regularity, modulation phenomena, intermittency and self-similarity, leading to efficient classification, detection and characterization of several pattern and multifractal texture recognition tasks. Although stability to geometric perturbations is necessary, it is not sufficient for the most challenging object recognition tasks, which require learning the invariance from data. We shall see that scattering operators can be generalized to this scenario, highlighting the close links between structured dictionary learning approaches and deep neural networks.
Joan Bruna graduated from Universitat Politècnica de Catalunya in both Mathematics and Electrical Engineering, in 2002 and 2004 respectively. He obtained an MSc in applied mathematics from ENS Cachan in 2005. From 2005 to 2012, he was a research engineer in an image processing startup, developing realtime video processing algorithms resulting in a dozen international patents. In 2013 he obtained his PhD in Applied Mathematics at École Polytechnique, under the supervision of Prof. Stéphane Mallat. Since fall 2012 he is a postdoctoral researcher in Yann LeCun's lab at the Courant Institute, New York. His research interests include invariant signal representations, stochastic processes, harmonic analysis, deep learning, and its applications to computer vision.
In-person attendance only available to those with active NYU ID cards.