pwc-archive/papers-with-abstracts huggingface dataset:
Synthetic Lung Nodule 3D Image Generation Using Autoencoders
Authors: Steve Kommrusch, Louis-Noël Pouchet
Date: 2018-11-19 00:00:00
Tasks: BIG-bench Machine Learning, Image Generation, Lung Cancer Diagnosis

One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train. A representative example is automated lung cancer diagnosis, where nodule images need to be classified as suspicious or benign. In this work we propose an automatic synthetic lung nodule image generator. Our 3D shape generator is designed to augment the variety of 3D images. Our proposed system takes root in autoencoder techniques, and we provide extensive experimental characterization that demonstrates its ability to produce quality synthetic images.
Generalized Zero-Shot Recognition based on Visually Semantic Embedding
Authors: Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama
Date: 2018-11-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute, Generalized Zero-Shot Learning, Zero-Shot Learning

We propose a novel Generalized Zero-Shot learning (GZSL) method that is
agnostic to both unseen images and unseen semantic vectors during training.
Prior works in this context propose to map high-dimensional visual features to
the semantic domain, we believe contributes to the semantic gap. To bridge the
gap, we propose a novel low-dimensional embedding of visual instances that is
"visually semantic." Analogous to semantic data that quantifies the existence
of an attribute in the presented instance, components of our visual embedding
quantifies existence of a prototypical part-type in the presented instance. In
parallel, as a thought experiment, we quantify the impact of noisy semantic
data by utilizing a novel visual oracle to visually supervise a learner. These
factors, namely semantic noise, visual-semantic gap and label noise lead us to
propose a new graphical model for inference with pairwise interactions between
label, semantic data, and inputs. We tabulate results on a number of benchmark
datasets demonstrating significant improvement in accuracy over
state-of-the-art under both semantic and visual supervision.
Private Selection from Private Candidates
Authors: Jingcheng Liu, Kunal Talwar
Date: 2018-11-19 00:00:00
Tasks: Computational Efficiency, Hyperparameter Optimization

Differentially Private algorithms often need to select the best amongst many
candidate options. Classical works on this selection problem require that the
candidates' goodness, measured as a real-valued score function, does not change
by much when one person's data changes. In many applications such as
hyperparameter optimization, this stability assumption is much too strong. In
this work, we consider the selection problem under a much weaker stability
assumption on the candidates, namely that the score functions are
differentially private. Under this assumption, we present algorithms that are
near-optimal along the three relevant dimensions: privacy, utility and
computational efficiency.
  Our result can be seen as a generalization of the exponential mechanism and
its existing generalizations. We also develop an online version of our
algorithm, that can be seen as a generalization of the sparse vector technique
to this weaker stability assumption. We show how our results imply better
algorithms for hyperparameter selection in differentially private machine
learning, as well as for adaptive data analysis.
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis
Authors: Adam Kortylewski, Mario Wieser, Andreas Morel-Forster, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth, Thomas Vetter
Date: 2018-11-19 00:00:00
Tasks: Bayesian Inference, valid

Computer vision tasks are difficult because of the large variability in the
data that is induced by changes in light, background, partial occlusion as well
as the varying pose, texture, and shape of objects. Generative approaches to
computer vision allow us to overcome this difficulty by explicitly modeling the
physical image formation process. Using generative object models, the analysis
of an observed image is performed via Bayesian inference of the posterior
distribution. This conceptually simple approach tends to fail in practice
because of several difficulties stemming from sampling the posterior
distribution: high-dimensionality and multi-modality of the posterior
distribution as well as expensive simulation of the rendering process. The main
difficulty of sampling approaches in a computer vision context is choosing the
proposal distribution accurately so that maxima of the posterior are explored
early and the algorithm quickly converges to a valid image interpretation. In
this work, we propose to use a Bayesian Neural Network for estimating an image
dependent proposal distribution. Compared to a standard Gaussian random walk
proposal, this accelerates the sampler in finding regions of the posterior with
high value. In this way, we can significantly reduce the number of samples
needed to perform facial image analysis.
Mitigating Architectural Mismatch During the Evolutionary Synthesis of Deep Neural Networks
Authors: Audrey Chung, Paul Fieguth, Alexander Wong
Date: 2018-11-19 00:00:00

Evolutionary deep intelligence has recently shown great promise for producing
small, powerful deep neural network models via the organic synthesis of
increasingly efficient architectures over successive generations. Existing
evolutionary synthesis processes, however, have allowed the mating of parent
networks independent of architectural alignment, resulting in a mismatch of
network structures. We present a preliminary study into the effects of
architectural alignment during evolutionary synthesis using a gene tagging
system. Surprisingly, the network architectures synthesized using the gene
tagging approach resulted in slower decreases in performance accuracy and
storage size; however, the resultant networks were comparable in size and
performance accuracy to the non-gene tagging networks. Furthermore, we
speculate that there is a noticeable decrease in network variability for
networks synthesized with gene tagging, indicating that enforcing a
like-with-like mating policy potentially restricts the exploration of the
search space of possible network architectures.
Tukey-Inspired Video Object Segmentation
Authors: Brent A. Griffin, Jason J. Corso
Date: 2018-11-19 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

We investigate the problem of strictly unsupervised video object
segmentation, i.e., the separation of a primary object from background in video
without a user-provided object mask or any training on an annotated dataset. We
find foreground objects in low-level vision data using a John Tukey-inspired
measure of "outlierness". This Tukey-inspired measure also estimates the
reliability of each data source as video characteristics change (e.g., a camera
starts moving). The proposed method achieves state-of-the-art results for
strictly unsupervised video object segmentation on the challenging DAVIS
dataset. Finally, we use a variant of the Tukey-inspired measure to combine the
output of multiple segmentation methods, including those using supervision
during training, runtime, or both. This collectively more robust method of
segmentation improves the Jaccard measure of its constituent methods by as much
as 28%.
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding
Authors: Yao Li, Martin Renqiang Min, Wenchao Yu, Cho-Jui Hsieh, Thomas C. M. Lee, Erik Kruus
Date: 2018-11-19 00:00:00
Tasks: Adversarial Attack, General Classification

Recent studies have demonstrated the vulnerability of deep convolutional
neural networks against adversarial examples. Inspired by the observation that
the intrinsic dimension of image data is much smaller than its pixel space
dimension and the vulnerability of neural networks grows with the input
dimension, we propose to embed high-dimensional input images into a
low-dimensional space to perform classification. However, arbitrarily
projecting the input images to a low-dimensional space without regularization
will not improve the robustness of deep neural networks. Leveraging optimal
transport theory, we propose a new framework, Optimal Transport Classifier
(OT-Classifier), and derive an objective that minimizes the discrepancy between
the distribution of the true label and the distribution of the OT-Classifier
output. Experimental results on several benchmark datasets show that, our
proposed framework achieves state-of-the-art performance against strong
adversarial attack methods.
Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery
Authors: Mo Deng, Shuai Li, George Barbastathis
Date: 2018-11-19 00:00:00
Tasks: Image Reconstruction, Retrieval, Super-Resolution

Deep Neural Network (DNN)-based image reconstruction, despite many successes,
often exhibits uneven fidelity between high and low spatial frequency bands. In
this paper we propose the Learning Synthesis by DNN (LS-DNN) approach where two
DNNs process the low and high spatial frequencies, respectively, and, improving
over [30], the two DNNs are trained separately and a third DNN combines them
into an image with high fidelity at all bands. We demonstrate LS-DNN in two
canonical inverse problems: super-resolution (SR) in diffraction-limited
imaging (DLI), and quantitative phase retrieval (QPR). Our results also show
comparable or improved performance over perceptual-loss based SR [21], and can
be generalized to a wider range of image recovery problems.
EFSIS: Ensemble Feature Selection Integrating Stability
Authors: Xiaokang Zhang, Inge Jonassen
Date: 2018-11-19 00:00:00
Tasks: Ensemble Learning, feature selection

Ensemble learning that can be used to combine the predictions from multiple
learners has been widely applied in pattern recognition, and has been reported
to be more robust and accurate than the individual learners. This ensemble
logic has recently also been more applied in feature selection. There are
basically two strategies for ensemble feature selection, namely data
perturbation and function perturbation. Data perturbation performs feature
selection on data subsets sampled from the original dataset and then selects
the features consistently ranked highly across those data subsets. This has
been found to improve both the stability of the selector and the prediction
accuracy for a classifier. Function perturbation frees the user from having to
decide on the most appropriate selector for any given situation and works by
aggregating multiple selectors. This has been found to maintain or improve
classification performance. Here we propose a framework, EFSIS, combining these
two strategies. Empirical results indicate that EFSIS gives both high
prediction accuracy and stability.
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection
Authors: Vivian Lai, Chenhao Tan
Date: 2018-11-19 00:00:00
Tasks: BIG-bench Machine Learning, Deception Detection, Medical Diagnosis

Humans are the final decision makers in critical tasks that involve ethical
and legal concerns, ranging from recidivism prediction, to medical diagnosis,
to fighting against fake news. Although machine learning models can sometimes
achieve impressive performance in these tasks, these tasks are not amenable to
full automation. To realize the potential of machine learning for improving
human decisions, it is important to understand how assistance from machine
learning models affects human performance and human agency.
  In this paper, we use deception detection as a testbed and investigate how we
can harness explanations and predictions of machine learning models to improve
human performance while retaining human agency. We propose a spectrum between
full human agency and full automation, and develop varying levels of machine
assistance along the spectrum that gradually increase the influence of machine
predictions. We find that without showing predicted labels, explanations alone
slightly improve human performance in the end task. In comparison, human
performance is greatly improved by showing predicted labels (>20% relative
improvement) and can be further improved by explicitly suggesting strong
machine performance. Interestingly, when predicted labels are shown,
explanations of machine predictions induce a similar level of accuracy as an
explicit statement of strong machine performance. Our results demonstrate a
tradeoff between human performance and human agency and show that explanations
of machine predictions can moderate this tradeoff.
Slum Segmentation and Change Detection : A Deep Learning Approach
Authors: Shishira R Maiya, Sudharshan Chandra Babu
Date: 2018-11-19 00:00:00
Tasks: Change Detection, Deep Learning, Instance Segmentation, Semantic Segmentation, Transfer Learning

More than one billion people live in slums around the world. In some
developing countries, slum residents make up for more than half of the
population and lack reliable sanitation services, clean water, electricity,
other basic services. Thus, slum rehabilitation and improvement is an important
global challenge, and a significant amount of effort and resources have been
put into this endeavor. These initiatives rely heavily on slum mapping and
monitoring, and it is essential to have robust and efficient methods for
mapping and monitoring existing slum settlements. In this work, we introduce an
approach to segment and map individual slums from satellite imagery, leveraging
regional convolutional neural networks for instance segmentation using transfer
learning. In addition, we also introduce a method to perform change detection
and monitor slum change over time. We show that our approach effectively learns
slum shape and appearance, and demonstrates strong quantitative results,
resulting in a maximum AP of 80.0.
IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet
Authors: Jose Dolz, Christian Desrosiers, Ismail Ben Ayed
Date: 2018-11-19 00:00:00
Tasks: GPU, Medical Image Segmentation, Segmentation

Accurate localization and segmentation of intervertebral disc (IVD) is
crucial for the assessment of spine disease diagnosis. Despite the
technological advances in medical imaging, IVD localization and segmentation
are still manually performed, which is time-consuming and prone to errors. If,
in addition, multi-modal imaging is considered, the burden imposed on disease
assessments increases substantially. In this paper, we propose an architecture
for IVD localization and segmentation in multi-modal MRI, which extends the
well-known UNet. Compared to single images, multi-modal data brings
complementary information, contributing to better data representation and
discriminative power. Our contributions are three-fold. First, how to
effectively integrate and fully leverage multi-modal data remains almost
unexplored. In this work, each MRI modality is processed in a different path to
better exploit their unique information. Second, inspired by HyperDenseNet, the
network is densely-connected both within each path and across different paths,
granting the model the freedom to learn where and how the different modalities
should be processed and combined. Third, we improved standard U-Net modules by
extending inception modules with two dilated convolutions blocks of different
scale, which helps handling multi-scale context. We report experiments over the
data set of the public MICCAI 2018 Challenge on Automatic Intervertebral Disc
Localization and Segmentation, with 13 multi-modal MRI images used for training
and 3 for validation. We trained IVD-Net on an NVidia TITAN XP GPU with 16 GBs
RAM, using ADAM as optimizer and a learning rate of 10e-5 during 200 epochs.
Training took about 5 hours, and segmentation of a whole volume about 2-3
seconds, on average. Several baselines, with different multi-modal fusion
strategies, were used to demonstrate the effectiveness of the proposed
architecture.
Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks
Authors: Sung Ho Kang, Kiwan Jeon, Hak-Jin Kim, Jin Keun Seo, Sang-Hwy Lee
Date: 2018-11-19 00:00:00

Background: Three-dimensional (3D) cephalometric analysis using computerized
tomography data has been rapidly adopted for dysmorphosis and anthropometry.
Several different approaches to automatic 3D annotation have been proposed to
overcome the limitations of traditional cephalometry. The purpose of this study
was to evaluate the accuracy of our newly-developed system using a deep
learning algorithm for automatic 3D cephalometric annotation. Methods: To
overcome current technical limitations, some measures were developed to
directly annotate 3D human skull data. Our deep learning-based model system
mainly consisted of a 3D convolutional neural network and image data
resampling. Results: The discrepancies between the referenced and predicted
coordinate values in three axes and in 3D distance were calculated to evaluate
system accuracy. Our new model system yielded prediction errors of 3.26, 3.18,
and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no
difference among the landmarks of the three groups, including the midsagittal
plane, horizontal plane, and mandible (p>0.05). Conclusion: A new 3D
convolutional neural network-based automatic annotation system for 3D
cephalometry was developed. The strategies used to implement the system were
detailed and measurement results were evaluated for accuracy. Further
development of this system is planned for full clinical application of
automatic 3D cephalometric annotation.
Self-Organizing Maps for Storage and Transfer of Knowledge in Reinforcement Learning
Authors: Thommen George Karimpanal, Roland Bouffanais
Date: 2018-11-18 00:00:00
Tasks: Continual Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The idea of reusing or transferring information from previously learned tasks
(source tasks) for the learning of new tasks (target tasks) has the potential
to significantly improve the sample efficiency of a reinforcement learning
agent. In this work, we describe a novel approach for reusing previously
acquired knowledge by using it to guide the exploration of an agent while it
learns new tasks. In order to do so, we employ a variant of the growing
self-organizing map algorithm, which is trained using a measure of similarity
that is defined directly in the space of the vectorized representations of the
value functions. In addition to enabling transfer across tasks, the resulting
map is simultaneously used to enable the efficient storage of previously
acquired task knowledge in an adaptive and scalable manner. We empirically
validate our approach in a simulated navigation environment, and also
demonstrate its utility through simple experiments using a mobile
micro-robotics platform. In addition, we demonstrate the scalability of this
approach, and analytically examine its relation to the proposed network growth
mechanism. Further, we briefly discuss some of the possible improvements and
extensions to this approach, as well as its relevance to real world scenarios
in the context of continual learning.
An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes
Authors: Junjie Huang, Wei Zou, Zheng Zhu, Jiagang Zhu
Date: 2018-11-18 00:00:00
Tasks: Motion Detection, Motion Detection In Non-Stationary Scenes, Optical Flow Estimation

Real-time motion detection in non-stationary scenes is a difficult task due
to dynamic background, changing foreground appearance and limited computational
resource. These challenges degrade the performance of the existing methods in
practical applications. In this paper, an optical flow based framework is
proposed to address this problem. By applying a novel strategy to utilize
optical flow, we enable our method being free of model constructing, training
or updating and can be performed efficiently. Besides, a dual judgment
mechanism with adaptive intervals and adaptive thresholds is designed to
heighten the system's adaptation to different situations. In experiment part,
we quantitatively and qualitatively validate the effectiveness and feasibility
of our method with videos in various scene conditions. The experimental results
show that our method adapts itself to different situations and outperforms the
state-of-the-art real-time methods, indicating the advantages of our optical
flow based method.
Accelerating the Evolution of Convolutional Neural Networks with Node-Level Mutations and Epigenetic Weight Initialization
Authors: Travis Desell
Date: 2018-11-17 00:00:00

This paper examines three generic strategies for improving the performance of
neuro-evolution techniques aimed at evolving convolutional neural networks
(CNNs). These were implemented as part of the Evolutionary eXploration of
Augmenting Convolutional Topologies (EXACT) algorithm. EXACT evolves arbitrary
convolutional neural networks (CNNs) with goals of better discovering and
understanding new effective architectures of CNNs for machine learning tasks
and to potentially automate the process of network design and selection. The
strategies examined are node-level mutation operations, epigenetic weight
initialization and pooling connections. Results were gathered over the period
of a month using a volunteer computing project, where over 225,000 CNNs were
trained and evaluated across 16 different EXACT searches. The node mutation
operations where shown to dramatically improve evolution rates over traditional
edge mutation operations (as used by the NEAT algorithm), and epigenetic weight
initialization was shown to further increase the accuracy and generalizability
of the trained CNNs. As a negative but interesting result, allowing for pooling
connections was shown to degrade the evolution progress. The best trained CNNs
reached 99.46% accuracy on the MNIST test data in under 13,500 CNN evaluations
-- accuracy comparable with some of the best human designed CNNs.
Chemical Structure Elucidation from Mass Spectrometry by Matching Substructures
Authors: Jing Lim, Joshua Wong, Minn Xuan Wong, Lee Han Eric Tan, Hai Leong Chieu, Davin Choo, Neng Kai Nigel Neo
Date: 2018-11-17 00:00:00

Chemical structure elucidation is a serious bottleneck in analytical
chemistry today. We address the problem of identifying an unknown chemical
threat given its mass spectrum and its chemical formula, a task which might
take well trained chemists several days to complete. Given a chemical formula,
there could be over a million possible candidate structures. We take a data
driven approach to rank these structures by using neural networks to predict
the presence of substructures given the mass spectrum, and matching these
substructures to the candidate structures. Empirically, we evaluate our
approach on a data set of chemical agents built for unknown chemical threat
identification. We show that our substructure classifiers can attain over 90%
micro F1-score, and we can find the correct structure among the top 20
candidates in 88% and 71% of test cases for two compound classes.
Finite Mixture Model of Nonparametric Density Estimation using Sampling Importance Resampling for Persistence Landscape
Authors: Farzad Eskandari, Soroush Pakniat
Date: 2018-11-17 00:00:00
Tasks: Density Estimation

Considering the creation of persistence landscape on a parametrized curve and
structure of sampling, there exists a random process for which a finite mixture
model of persistence landscape (FMMPL) can provide a better description for a
given dataset. In this paper, a nonparametric approach for computing integrated
mean of square error (IMSE) in persistence landscape has been presented. As a
result, FMMPL is more accurate than the another way. Also, the sampling
importance resampling (SIR) has been presented a better description of
important landmark from parametrized curve. The result, provides more accuracy
and less space complexity than the landmarks selected with simple sampling.