121 research outputs found
A Systematic Survey of Regularization and Normalization in GANs
Generative Adversarial Networks (GANs) have been widely applied in different
scenarios thanks to the development of deep neural networks. The original GAN
was proposed based on the non-parametric assumption of the infinite capacity of
networks. However, it is still unknown whether GANs can generate realistic
samples without any prior information. Due to the overconfident assumption,
many issues remain unaddressed in GANs' training, such as non-convergence, mode
collapses, gradient vanishing. Regularization and normalization are common
methods of introducing prior information to stabilize training and improve
discrimination. Although a handful number of regularization and normalization
methods have been proposed for GANs, to the best of our knowledge, there exists
no comprehensive survey which primarily focuses on objectives and development
of these methods, apart from some in-comprehensive and limited scope studies.
In this work, we conduct a comprehensive survey on the regularization and
normalization techniques from different perspectives of GANs training. First,
we systematically describe different perspectives of GANs training and thus
obtain the different objectives of regularization and normalization. Based on
these objectives, we propose a new taxonomy. Furthermore, we compare the
performance of the mainstream methods on different datasets and investigate the
regularization and normalization techniques that have been frequently employed
in SOTA GANs. Finally, we highlight potential future directions of research in
this domain
On gradient regularizers for MMD GANs
We propose a principled method for gradient-based regularization of the critic of
GAN-like models trained by adversarially optimizing the kernel of a Maximum
Mean Discrepancy (MMD). We show that controlling the gradient of the critic
is vital to having a sensible loss function, and devise a method to enforce exact,
analytical gradient constraints at no additional cost compared to existing approximate
techniques based on additive regularizers. The new loss function is provably
continuous, and experiments show that it stabilizes and accelerates training, giving
image generation models that outperform state-of-the art methods on 160 × 160
CelebA and 64 × 64 unconditional ImageNet
Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning
Machine learning provides a novel avenue for the study of experimental
realizations of many-body systems, and has recently been proven successful in
analyzing properties of experimental data of ultracold quantum gases. We here
show that deep learning succeeds in the more challenging task of modelling such
an experimental data distribution. Our generative model (RUGAN) is able to
produce snapshots of a doped two-dimensional Fermi-Hubbard model that are
indistinguishable from previously reported experimental realizations.
Importantly, it is capable of accurately generating snapshots at conditions for
which it did not observe any experimental data, such as at higher doping
values. On top of that, our generative model extracts relevant patterns from
small-scale examples and can use these to construct new configurations at a
larger size that serve as a precursor to observations at scales that are
currently experimentally inaccessible. The snapshots created by our
model---which come at effectively no cost---are extremely useful as they can be
employed to quantitatively test new theoretical developments under conditions
that have not been explored experimentally, parameterize phenomenological
models, or train other, more data-intensive, machine learning methods. We
provide predictions for experimental observables at unobserved conditions and
benchmark these against modern theoretical frameworks. The deep learning method
we develop here is broadly applicable and can be used for the efficient
large-scale simulation of equilibrium and nonequilibrium physical systems
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