5 research outputs found
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
In unsupervised data generation tasks, besides the generation of a sample
based on previous observations, one would often like to give hints to the model
in order to bias the generation towards desirable metrics. We propose a method
that combines Generative Adversarial Networks (GANs) and reinforcement learning
(RL) in order to accomplish exactly that. While RL biases the data generation
process towards arbitrary metrics, the GAN component of the reward function
ensures that the model still remembers information learned from data. We build
upon previous results that incorporated GANs and RL in order to generate
sequence data and test this model in several settings for the generation of
molecules encoded as text sequences (SMILES) and in the context of music
generation, showing for each case that we can effectively bias the generation
process towards desired metrics.Comment: 10 pages, 7 figure
Classification of sparsely labeled spatio-temporal data through semi-supervised adversarial learning
In recent years, Generative Adversarial Networks (GAN) have emerged as a
powerful method for learning the mapping from noisy latent spaces to realistic
data samples in high-dimensional space. So far, the development and application
of GANs have been predominantly focused on spatial data such as images. In this
project, we aim at modeling of spatio-temporal sensor data instead, i.e.
dynamic data over time. The main goal is to encode temporal data into a global
and low-dimensional latent vector that captures the dynamics of the
spatio-temporal signal. To this end, we incorporate auto-regressive RNNs,
Wasserstein GAN loss, spectral norm weight constraints and a semi-supervised
learning scheme into InfoGAN, a method for retrieval of meaningful latents in
adversarial learning. To demonstrate the modeling capability of our method, we
encode full-body skeletal human motion from a large dataset representing 60
classes of daily activities, recorded in a multi-Kinect setup. Initial results
indicate competitive classification performance of the learned latent
representations, compared to direct CNN/RNN inference. In future work, we plan
to apply this method on a related problem in the medical domain, i.e. on
recovery of meaningful latents in gait analysis of patients with vertigo and
balance disorders
Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data
Electronic health records (EHRs) have contributed to the computerization of
patient records and can thus be used not only for efficient and systematic
medical services, but also for research on biomedical data science. However,
there are many missing values in EHRs when provided in matrix form, which is an
important issue in many biomedical EHR applications. In this paper, we propose
a two-stage framework that includes missing data imputation and disease
prediction to address the missing data problem in EHRs. We compared the disease
prediction performance of generative adversarial networks (GANs) and
conventional learning algorithms in combination with missing data prediction
methods. As a result, we obtained a level of accuracy of 0.9777, sensitivity of
0.9521, specificity of 0.9925, area under the receiver operating characteristic
curve (AUC-ROC) of 0.9889, and F-score of 0.9688 with a stacked autoencoder as
the missing data prediction method and an auxiliary classifier GAN (AC-GAN) as
the disease prediction method. The comparison results show that a combination
of a stacked autoencoder and an AC-GAN significantly outperforms other existing
approaches. Our results suggest that the proposed framework is more robust for
disease prediction from EHRs with missing data.Comment: 10 pages, 4 figure
Domain Adaptation Using Adversarial Learning for Autonomous Navigation
Autonomous navigation has become an increasingly popular machine learning
application. Recent advances in deep learning have also resulted in great
improvements to autonomous navigation. However, prior outdoor autonomous
navigation depends on various expensive sensors or large amounts of real
labeled data which is difficult to acquire and sometimes erroneous. The
objective of this study is to train an autonomous navigation model that uses a
simulator (instead of real labeled data) and an inexpensive monocular camera.
In order to exploit the simulator satisfactorily, our proposed method is based
on domain adaptation with adversarial learning. Specifically, we propose our
model with 1) a dilated residual block in the generator, 2) cycle loss, and 3)
style loss to improve the adversarial learning performance for satisfactory
domain adaptation. In addition, we perform a theoretical analysis that supports
the justification of our proposed method. We present empirical results of
navigation in outdoor courses with various intersections using a commercial
radio controlled car. We observe that our proposed method allows us to learn a
favorable navigation model by generating images with realistic textures. To the
best of our knowledge, this is the first work to apply domain adaptation with
adversarial learning to autonomous navigation in real outdoor environments. Our
proposed method can also be applied to precise image generation or other
robotic tasks
How Generative Adversarial Networks and Their Variants Work: An Overview
Generative Adversarial Networks (GAN) have received wide attention in the
machine learning field for their potential to learn high-dimensional, complex
real data distribution. Specifically, they do not rely on any assumptions about
the distribution and can generate real-like samples from latent space in a
simple manner. This powerful property leads GAN to be applied to various
applications such as image synthesis, image attribute editing, image
translation, domain adaptation and other academic fields. In this paper, we aim
to discuss the details of GAN for those readers who are familiar with, but do
not comprehend GAN deeply or who wish to view GAN from various perspectives. In
addition, we explain how GAN operates and the fundamental meaning of various
objective functions that have been suggested recently. We then focus on how the
GAN can be combined with an autoencoder framework. Finally, we enumerate the
GAN variants that are applied to various tasks and other fields for those who
are interested in exploiting GAN for their research.Comment: 41 pages, 16 figures, Published in ACM Computing Surveys (CSUR