9 research outputs found
Gabor frames and deep scattering networks in audio processing
This paper introduces Gabor scattering, a feature extractor based on Gabor
frames and Mallat's scattering transform. By using a simple signal model for
audio signals specific properties of Gabor scattering are studied. It is shown
that for each layer, specific invariances to certain signal characteristics
occur. Furthermore, deformation stability of the coefficient vector generated
by the feature extractor is derived by using a decoupling technique which
exploits the contractivity of general scattering networks. Deformations are
introduced as changes in spectral shape and frequency modulation. The
theoretical results are illustrated by numerical examples and experiments.
Numerical evidence is given by evaluation on a synthetic and a "real" data set,
that the invariances encoded by the Gabor scattering transform lead to higher
performance in comparison with just using Gabor transform, especially when few
training samples are available.Comment: 26 pages, 8 figures, 4 tables. Repository for reproducibility:
https://gitlab.com/hararticles/gs-gt . Keywords: machine learning; scattering
transform; Gabor transform; deep learning; time-frequency analysis; CNN.
Accepted and published after peer revisio
FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer
Particle localization and -classification constitute two of the most
fundamental problems in computational microscopy. In recent years, deep
learning based approaches have been introduced for these tasks with great
success. A key shortcoming of these supervised learning methods is their need
for large training data sets, typically generated from particle models in
conjunction with complex numerical forward models simulating the physics of
transmission electron microscopes. Computer implementations of such forward
models are computationally extremely demanding and limit the scope of their
applicability. In this paper we propose a method for simulating the forward
operator of an electron microscope based on additive noise and Neural Style
Transfer techniques. We evaluate the method on localization and classification
tasks using one of the established state-of-the-art architectures showing
performance on par with the benchmark. In contrast to previous approaches, our
method accelerates the data generation process by a factor of 750 while using
33 times less memory and scales well to typical transmission electron
microscope detector sizes. It utilizes GPU acceleration and parallel
processing. It can be used to adapt a synthetic training data set according to
reference data from any transmission electron microscope. The source code is
available at https://gitlab.com/deepet/faket.Comment: 18 pages, 1 table, 16 figures. Included fine-tuning, ablation, and
noiseless experiment
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems
The use of orthogonal projections on high-dimensional input and target data
in learning frameworks is studied. First, we investigate the relations between
two standard objectives in dimension reduction, preservation of variance and of
pairwise relative distances. Investigations of their asymptotic correlation as
well as numerical experiments show that a projection does usually not satisfy
both objectives at once. In a standard classification problem we determine
projections on the input data that balance the objectives and compare
subsequent results. Next, we extend our application of orthogonal projections
to deep learning tasks and introduce a general framework of augmented target
loss functions. These loss functions integrate additional information via
transformations and projections of the target data. In two supervised learning
problems, clinical image segmentation and music information classification, the
application of our proposed augmented target loss functions increase the
accuracy
Gabor Frames and Deep Scattering Networks in Audio Processing
This paper introduces Gabor scattering, a feature extractor based on Gabor frames and Mallat’s scattering transform. By using a simple signal model for audio signals, specific properties of Gabor scattering are studied. It is shown that, for each layer, specific invariances to certain signal characteristics occur. Furthermore, deformation stability of the coefficient vector generated by the feature extractor is derived by using a decoupling technique which exploits the contractivity of general scattering networks. Deformations are introduced as changes in spectral shape and frequency modulation. The theoretical results are illustrated by numerical examples and experiments. Numerical evidence is given by evaluation on a synthetic and a “real” dataset, that the invariances encoded by the Gabor scattering transform lead to higher performance in comparison with just using Gabor transform, especially when few training samples are available.© 2019 by the author