3,013 research outputs found
On Noise Injection in Generative Adversarial Networks
Noise injection has been proved to be one of the key technique advances in
generating high-fidelity images. Despite its successful usage in GANs, the
mechanism of its validity is still unclear. In this paper, we propose a
geometric framework to theoretically analyze the role of noise injection in
GANs. Based on Riemannian geometry, we successfully model the noise injection
framework as fuzzy equivalence on the geodesic normal coordinates. Guided by
our theories, we find that the existing method is incomplete and a new strategy
for noise injection is devised. Experiments on image generation and GAN
inversion demonstrate the superiority of our method
Distributed generation of privacy preserving data with user customization
Distributed devices such as mobile phones can produce and store large amounts
of data that can enhance machine learning models; however, this data may
contain private information specific to the data owner that prevents the
release of the data. We wish to reduce the correlation between user-specific
private information and data while maintaining the useful information. Rather
than learning a large model to achieve privatization from end to end, we
introduce a decoupling of the creation of a latent representation and the
privatization of data that allows user-specific privatization to occur in a
distributed setting with limited computation and minimal disturbance on the
utility of the data. We leverage a Variational Autoencoder (VAE) to create a
compact latent representation of the data; however, the VAE remains fixed for
all devices and all possible private labels. We then train a small generative
filter to perturb the latent representation based on individual preferences
regarding the private and utility information. The small filter is trained by
utilizing a GAN-type robust optimization that can take place on a distributed
device. We conduct experiments on three popular datasets: MNIST, UCI-Adult, and
CelebA, and give a thorough evaluation including visualizing the geometry of
the latent embeddings and estimating the empirical mutual information to show
the effectiveness of our approach.Comment: accepted in ICLR 2019 SafeML worksho
DeepFlow: History Matching in the Space of Deep Generative Models
The calibration of a reservoir model with observed transient data of fluid
pressures and rates is a key task in obtaining a predictive model of the flow
and transport behaviour of the earth's subsurface. The model calibration task,
commonly referred to as "history matching", can be formalised as an ill-posed
inverse problem where we aim to find the underlying spatial distribution of
petrophysical properties that explain the observed dynamic data. We use a
generative adversarial network pretrained on geostatistical object-based models
to represent the distribution of rock properties for a synthetic model of a
hydrocarbon reservoir. The dynamic behaviour of the reservoir fluids is
modelled using a transient two-phase incompressible Darcy formulation. We
invert for the underlying reservoir properties by first modeling property
distributions using the pre-trained generative model then using the adjoint
equations of the forward problem to perform gradient descent on the latent
variables that control the output of the generative model. In addition to the
dynamic observation data, we include well rock-type constraints by introducing
an additional objective function. Our contribution shows that for a synthetic
test case, we are able to obtain solutions to the inverse problem by optimising
in the latent variable space of a deep generative model, given a set of
transient observations of a non-linear forward problem.Comment: 25 pages, 15 figures, fixed typo
FaR-GAN for One-Shot Face Reenactment
Animating a static face image with target facial expressions and movements is
important in the area of image editing and movie production. This face
reenactment process is challenging due to the complex geometry and movement of
human faces. Previous work usually requires a large set of images from the same
person to model the appearance. In this paper, we present a one-shot face
reenactment model, FaR-GAN, that takes only one face image of any given source
identity and a target expression as input, and then produces a face image of
the same source identity but with the target expression. The proposed method
makes no assumptions about the source identity, facial expression, head pose,
or even image background. We evaluate our method on the VoxCeleb1 dataset and
show that our method is able to generate a higher quality face image than the
compared methods.Comment: This paper has been accepted to the AI for content creation workshop
at CVPR 202
Multi-Mapping Image-to-Image Translation with Central Biasing Normalization
Recent advances in image-to-image translation have seen a rise in approaches
generating diverse images through a single network. To indicate the target
domain for a one-to-many mapping, the latent code is injected into the
generator network. However, we found that the injection method leads to mode
collapse because of normalization strategies. Existing normalization strategies
might either cause the inconsistency of feature distribution or eliminate the
effect of the latent code. To solve these problems, we propose the consistency
within diversity criteria for designing the multi-mapping model. Based on the
criteria, we propose central biasing normalization to inject the latent code
information. Experiments show that our method can improve the quality and
diversity of existing image-to-image translation models, such as StarGAN,
BicycleGAN, and pix2pix
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks
Generative Adversarial Networks (GANs) have shown considerable promise for
mitigating the challenge of data scarcity when building machine learning-driven
analysis algorithms. Specifically, a number of studies have shown that
GAN-based image synthesis for data augmentation can aid in improving
classification accuracy in a number of medical image analysis tasks, such as
brain and liver image analysis. However, the efficacy of leveraging GANs for
tackling prostate cancer analysis has not been previously explored. Motivated
by this, in this study we introduce ProstateGAN, a GAN-based model for
synthesizing realistic prostate diffusion imaging data. More specifically, in
order to generate new diffusion imaging data corresponding to a particular
cancer grade (Gleason score), we propose a conditional deep convolutional GAN
architecture that takes Gleason scores into consideration during the training
process. Experimental results show that high-quality synthetic prostate
diffusion imaging data can be generated using the proposed ProstateGAN for
specified Gleason scores.Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
arXiv:1811.0721
SinGAN: Learning a Generative Model from a Single Natural Image
We introduce SinGAN, an unconditional generative model that can be learned
from a single natural image. Our model is trained to capture the internal
distribution of patches within the image, and is then able to generate high
quality, diverse samples that carry the same visual content as the image.
SinGAN contains a pyramid of fully convolutional GANs, each responsible for
learning the patch distribution at a different scale of the image. This allows
generating new samples of arbitrary size and aspect ratio, that have
significant variability, yet maintain both the global structure and the fine
textures of the training image. In contrast to previous single image GAN
schemes, our approach is not limited to texture images, and is not conditional
(i.e. it generates samples from noise). User studies confirm that the generated
samples are commonly confused to be real images. We illustrate the utility of
SinGAN in a wide range of image manipulation tasks.Comment: ICCV 201
SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning
Image translation is a burgeoning field in computer vision where the goal is
to learn the mapping between an input image and an output image. However, most
recent methods require multiple generators for modeling different domain
mappings, which are inefficient and ineffective on some multi-domain image
translation tasks. In this paper, we propose a novel method, SingleGAN, to
perform multi-domain image-to-image translations with a single generator. We
introduce the domain code to explicitly control the different generative tasks
and integrate multiple optimization goals to ensure the translation.
Experimental results on several unpaired datasets show superior performance of
our model in translation between two domains. Besides, we explore variants of
SingleGAN for different tasks, including one-to-many domain translation,
many-to-many domain translation and one-to-one domain translation with
multimodality. The extended experiments show the universality and extensibility
of our model.Comment: Accepted in ACCV 2018. Code is available at
https://github.com/Xiaoming-Yu/SingleGA
Security and Privacy Issues in Deep Learning
With the development of machine learning (ML), expectations for artificial
intelligence (AI) technology have been increasing daily. In particular, deep
neural networks have shown outstanding performance results in many fields. Many
applications are deeply involved in our daily life, such as making significant
decisions in application areas based on predictions or classifications, in
which a DL model could be relevant. Hence, if a DL model causes mispredictions
or misclassifications due to malicious external influences, then it can cause
very large difficulties in real life. Moreover, training DL models involve an
enormous amount of data and the training data often include sensitive
information. Therefore, DL models should not expose the privacy of such data.
In this paper, we review the vulnerabilities and the developed defense methods
on the security of the models and data privacy under the notion of secure and
private AI (SPAI). We also discuss current challenges and open issues
Dual Adversarial Variational Embedding for Robust Recommendation
Robust recommendation aims at capturing true preference of users from noisy
data, for which there are two lines of methods have been proposed. One is based
on noise injection, and the other is to adopt the generative model Variational
Auto-encoder (VAE). However, the existing works still face two challenges.
First, the noise injection based methods often draw the noise from a fixed
noise distribution given in advance, while in real world, the noise
distributions of different users and items may differ from each other due to
personal behaviors and item usage patterns. Second, the VAE based models are
not expressive enough to capture the true preference since VAE often yields an
embedding space of a single modal, while in real world, user-item interactions
usually exhibit multi-modality on user preference distribution. In this paper,
we propose a novel model called Dual Adversarial Variational Embedding (DAVE)
for robust recommendation, which can provide personalized noise reduction for
different users and items, and capture the multi-modality of the embedding
space, by combining the advantages of VAE and adversarial training between the
introduced auxiliary discriminators and the variational inference networks. The
extensive experiments conducted on real datasets verify the effectiveness of
DAVE on robust recommendation
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