7,833 research outputs found
Generative Spatiotemporal Modeling Of Neutrophil Behavior
Cell motion and appearance have a strong correlation with cell cycle and
disease progression. Many contemporary efforts in machine learning utilize
spatio-temporal models to predict a cell's physical state and, consequently,
the advancement of disease. Alternatively, generative models learn the
underlying distribution of the data, creating holistic representations that can
be used in learning. In this work, we propose an aggregate model that combine
Generative Adversarial Networks (GANs) and Autoregressive (AR) models to
predict cell motion and appearance in human neutrophils imaged by differential
interference contrast (DIC) microscopy. We bifurcate the task of learning cell
statistics by leveraging GANs for the spatial component and AR models for the
temporal component. The aggregate model learned results offer a promising
computational environment for studying changes in organellar shape, quantity,
and spatial distribution over large sequences.Comment: 4 pages, Accepted to 2018 IEEE International Symposium on Biomedical
Imagin
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
The recent proliferation of fake portrait videos poses direct threats on
society, law, and privacy. Believing the fake video of a politician,
distributing fake pornographic content of celebrities, fabricating impersonated
fake videos as evidence in courts are just a few real world consequences of
deep fakes. We present a novel approach to detect synthetic content in portrait
videos, as a preventive solution for the emerging threat of deep fakes. In
other words, we introduce a deep fake detector. We observe that detectors
blindly utilizing deep learning are not effective in catching fake content, as
generative models produce formidably realistic results. Our key assertion
follows that biological signals hidden in portrait videos can be used as an
implicit descriptor of authenticity, because they are neither spatially nor
temporally preserved in fake content. To prove and exploit this assertion, we
first engage several signal transformations for the pairwise separation
problem, achieving 99.39% accuracy. Second, we utilize those findings to
formulate a generalized classifier for fake content, by analyzing proposed
signal transformations and corresponding feature sets. Third, we generate novel
signal maps and employ a CNN to improve our traditional classifier for
detecting synthetic content. Lastly, we release an "in the wild" dataset of
fake portrait videos that we collected as a part of our evaluation process. We
evaluate FakeCatcher on several datasets, resulting with 96%, 94.65%, 91.50%,
and 91.07% accuracies, on Face Forensics, Face Forensics++, CelebDF, and on our
new Deep Fakes Dataset respectively. We also analyze signals from various
facial regions, under image distortions, with varying segment durations, from
different generators, against unseen datasets, and under several dimensionality
reduction techniques.Comment: To appear in IEEE Transactions on Pattern Analysis and Machine
Intelligence (PAMI), accepted July 2020. Dataset: http://bit.ly/FakeCatche
MAGAN: Aligning Biological Manifolds
It is increasingly common in many types of natural and physical systems
(especially biological systems) to have different types of measurements
performed on the same underlying system. In such settings, it is important to
align the manifolds arising from each measurement in order to integrate such
data and gain an improved picture of the system. We tackle this problem using
generative adversarial networks (GANs). Recently, GANs have been utilized to
try to find correspondences between sets of samples. However, these GANs are
not explicitly designed for proper alignment of manifolds. We present a new GAN
called the Manifold-Aligning GAN (MAGAN) that aligns two manifolds such that
related points in each measurement space are aligned together. We demonstrate
applications of MAGAN in single-cell biology in integrating two different
measurement types together. In our demonstrated examples, cells from the same
tissue are measured with both genomic (single-cell RNA-sequencing) and
proteomic (mass cytometry) technologies. We show that the MAGAN successfully
aligns them such that known correlations between measured markers are improved
compared to other recently proposed models
Generative Creativity: Adversarial Learning for Bionic Design
Bionic design refers to an approach of generative creativity in which a
target object (e.g. a floor lamp) is designed to contain features of biological
source objects (e.g. flowers), resulting in creative biologically-inspired
design. In this work, we attempt to model the process of shape-oriented bionic
design as follows: given an input image of a design target object, the model
generates images that 1) maintain shape features of the input design target
image, 2) contain shape features of images from the specified biological source
domain, 3) are plausible and diverse. We propose DesignGAN, a novel
unsupervised deep generative approach to realising bionic design. Specifically,
we employ a conditional Generative Adversarial Networks architecture with
several designated losses (an adversarial loss, a regression loss, a cycle loss
and a latent loss) that respectively constrict our model to meet the
corresponding aforementioned requirements of bionic design modelling. We
perform qualitative and quantitative experiments to evaluate our method, and
demonstrate that our proposed approach successfully generates creative images
of bionic design
Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation
Emojis have become a very popular part of daily digital communication. Their
appeal comes largely in part due to their ability to capture and elicit
emotions in a more subtle and nuanced way than just plain text is able to. In
line with recent advances in the field of deep learning, there are far reaching
implications and applications that generative adversarial networks (GANs) can
have for image generation. In this paper, we present a novel application of
deep convolutional GANs (DC-GANs) with an optimized training procedure. We show
that via incorporation of word embeddings conditioned on Google's word2vec
model into the network, the generator is able to synthesize highly realistic
emojis that are virtually identical to the real ones.Comment: 5 pages, 3 figures, 2 graph
Synthesizing New Retinal Symptom Images by Multiple Generative Models
Age-Related Macular Degeneration (AMD) is an asymptomatic retinal disease
which may result in loss of vision. There is limited access to high-quality
relevant retinal images and poor understanding of the features defining
sub-classes of this disease. Motivated by recent advances in machine learning
we specifically explore the potential of generative modeling, using Generative
Adversarial Networks (GANs) and style transferring, to facilitate clinical
diagnosis and disease understanding by feature extraction. We design an
analytic pipeline which first generates synthetic retinal images from clinical
images; a subsequent verification step is applied. In the synthesizing step we
merge GANs (DCGANs and WGANs architectures) and style transferring for the
image generation, whereas the verified step controls the accuracy of the
generated images. We find that the generated images contain sufficient
pathological details to facilitate ophthalmologists' task of disease
classification and in discovery of disease relevant features. In particular,
our system predicts the drusen and geographic atrophy sub-classes of AMD.
Furthermore, the performance using CFP images for GANs outperforms the
classification based on using only the original clinical dataset. Our results
are evaluated using existing classifier of retinal diseases and class activated
maps, supporting the predictive power of the synthetic images and their utility
for feature extraction. Our code examples are available online
Quick and Easy Time Series Generation with Established Image-based GANs
In the recent years Generative Adversarial Networks (GANs) have demonstrated
significant progress in generating authentic looking data. In this work we
introduce our simple method to exploit the advancements in well established
image-based GANs to synthesise single channel time series data. We implement
Wasserstein GANs (WGANs) with gradient penalty due to their stability in
training to synthesise three different types of data; sinusoidal data,
photoplethysmograph (PPG) data and electrocardiograph (ECG) data. The length of
the returned time series data is limited only by the image resolution, we use
an image size of 64x64 pixels which yields 4096 data points. We present both
visual and quantitative evidence that our novel method can successfully
generate time series data using image-based GANs
Adversarial Video Compression Guided by Soft Edge Detection
We propose a video compression framework using conditional Generative
Adversarial Networks (GANs). We rely on two encoders: one that deploys a
standard video codec and another which generates low-level maps via a pipeline
of down-sampling, a newly devised soft edge detector, and a novel lossless
compression scheme. For decoding, we use a standard video decoder as well as a
neural network based one, which is trained using a conditional GAN. Recent
"deep" approaches to video compression require multiple videos to pre-train
generative networks to conduct interpolation. In contrast to this prior work,
our scheme trains a generative decoder on pairs of a very limited number of key
frames taken from a single video and corresponding low-level maps. The trained
decoder produces reconstructed frames relying on a guidance of low-level maps,
without any interpolation. Experiments on a diverse set of 131 videos
demonstrate that our proposed GAN-based compression engine achieves much higher
quality reconstructions at very low bitrates than prevailing standard codecs
such as H.264 or HEVC
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training
To realize the full potential of deep learning for medical imaging, large
annotated datasets are required for training. Such datasets are difficult to
acquire because labeled medical images are not usually available due to privacy
issues, lack of experts available for annotation, underrepresentation of rare
conditions and poor standardization. Lack of annotated data has been addressed
in conventional vision applications using synthetic images refined via
unsupervised adversarial training to look like real images. However, this
approach is difficult to extend to general medical imaging because of the
complex and diverse set of features found in real human tissues. We propose an
alternative framework that uses a reverse flow, where adversarial training is
used to make real medical images more like synthetic images, and hypothesize
that clinically-relevant features can be preserved via self-regularization.
These domain-adapted images can then be accurately interpreted by networks
trained on large datasets of synthetic medical images. We test this approach
for the notoriously difficult task of depth-estimation from endoscopy. We train
a depth estimator on a large dataset of synthetic images generated using an
accurate forward model of an endoscope and an anatomically-realistic colon.
This network predicts significantly better depths when using synthetic-like
domain-adapted images compared to the real images, confirming that the
clinically-relevant features of depth are preserved.Comment: 10 pages, 8 figur
COCO-GAN: Generation by Parts via Conditional Coordinating
Humans can only interact with part of the surrounding environment due to
biological restrictions. Therefore, we learn to reason the spatial
relationships across a series of observations to piece together the surrounding
environment. Inspired by such behavior and the fact that machines also have
computational constraints, we propose \underline{CO}nditional
\underline{CO}ordinate GAN (COCO-GAN) of which the generator generates images
by parts based on their spatial coordinates as the condition. On the other
hand, the discriminator learns to justify realism across multiple assembled
patches by global coherence, local appearance, and edge-crossing continuity.
Despite the full images are never generated during training, we show that
COCO-GAN can produce \textbf{state-of-the-art-quality} full images during
inference. We further demonstrate a variety of novel applications enabled by
teaching the network to be aware of coordinates. First, we perform
extrapolation to the learned coordinate manifold and generate off-the-boundary
patches. Combining with the originally generated full image, COCO-GAN can
produce images that are larger than training samples, which we called
"beyond-boundary generation". We then showcase panorama generation within a
cylindrical coordinate system that inherently preserves horizontally cyclic
topology. On the computation side, COCO-GAN has a built-in divide-and-conquer
paradigm that reduces memory requisition during training and inference,
provides high-parallelism, and can generate parts of images on-demand.Comment: Accepted to ICCV'19 (oral). All images are compressed due to size
limit, please access the full-resolution version via Google Drive:
http://bit.ly/COCO-GAN-ful
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