Talk: Deep learning for multivariate fractal texture synthesis

IEEE-IISc Student Branch
Signal Processing Society Chapter
Technical talk
Deep Learning for multivariate fractal texture synthesis:
Does it work? How do we know that it works?
Dr. Patrice Abry (IEEE Fellow)
Director of research, CNRS, ENS Lyon, France
Golden Jubilee Seminar Hall
ECE Dept., IISc
06/02/2020, 2.30-3.30 pm
All are Welcome
Coffee/tea 3.30pm
Abstract: Deep Convolutional Generative Adversarial Networks (DCGAN) have been widely used to
synthesize images. Their use remains however concentrated on geometrical images (such as
faces) and they have been much less used for the synthesis of textured images. Our aim is to
investigate the potential of DCGAN to generate multivariate textures. To that end, we make use of a
large set of synthetic multivariate multifractal textures, which consists of a collection of scale-free
(or fractal) textures with non-trivial cross-dependencies (cross-selfsimilarity and cross-
multifractality) to train a DCGAN. We make use of wavelet transforms and wavelet-leaders to
compare the quality of the DCGAN synthesized textures against those of the original textures. We
discuss reproductiblity and convergence issues. Joint work with : V. Mauduit, S. Roux, E.
Quemener, ENS Lyon, France
Patrice Abry (IEEE Fellow) was born in Bourg-en-Bresse, France in 1966. He received the degree of Professeur-Agr eg e de Sciences Physiques, in 1989 at Ecole Normale Sup erieure de Cachan and
completed a PhD in Physics and Signal Processing, at Université Claude-Bernard University in Lyon in 1994. He is a CNRS Senior Scientist, at the Physics dept. of Ecole Normale Superieure de Lyon,
where he is in charge of the Signal, systems and Physics research team. Patrice Abry received the AFCET-MESR-CNRS prize for best PhD in Signal Processing for the years 93-94 and has been elected IEEE Fellow in 2011. He is the author of a book in French dedicated to wavelet, scale invariance and hydrodynamic turbulence and is also the coeditor of a book entitled “Scaling, Fractals and Wavelets”. He has been elected IEEE fellow in 2011 and he serves as an elected member of the IEEE SPS Signal Processing Theory and Methods Technical Committee. His current research interests include wavelet-based analysis and modeling of statistical scale-free dynamics (self-similarity, stable processes, multi-fractal, 1/f processes, long-range dependence, local regularity of processes, infinitely divisible cascades, departures from exact scale invariance). Beyond theoretical developments and contributions in multifractal analysis and stochastic process design. Patrice Abry shows a strong interest into real-world applications, such as hydrodynamic turbulence, computer network teletraffic, heart rate variability.

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