pwc-archive/papers-with-abstracts huggingface dataset:
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification
Authors: Xiaofeng Zhang, Zhangyang Wang, Dong Liu, Qing Ling
Date: 2018-08-29 00:00:00
Tasks: Data Augmentation, General Classification, Generative Adversarial Network, Transfer Learning

Deep learning has revolutionized the performance of classification, but
meanwhile demands sufficient labeled data for training. Given insufficient
data, while many techniques have been developed to help combat overfitting, the
challenge remains if one tries to train deep networks, especially in the
ill-posed extremely low data regimes: only a small set of labeled data are
available, and nothing -- including unlabeled data -- else. Such regimes arise
from practical situations where not only data labeling but also data collection
itself is expensive. We propose a deep adversarial data augmentation (DADA)
technique to address the problem, in which we elaborately formulate data
augmentation as a problem of training a class-conditional and supervised
generative adversarial network (GAN). Specifically, a new discriminator loss is
proposed to fit the goal of data augmentation, through which both real and
augmented samples are enforced to contribute to and be consistent in finding
the decision boundaries. Tailored training techniques are developed
accordingly. To quantitatively validate its effectiveness, we first perform
extensive simulations to show that DADA substantially outperforms both
traditional data augmentation and a few GAN-based options. We then extend
experiments to three real-world small labeled datasets where existing data
augmentation and/or transfer learning strategies are either less effective or
infeasible. All results endorse the superior capability of DADA in enhancing
the generalization ability of deep networks trained in practical extremely low
data regimes. Source code is available at
https://github.com/SchafferZhang/DADA.
Wavelet based edge feature enhancement for convolutional neural networks
Authors: D. D. N. De Silva, S. Fernando, I. T. S. Piyatilake, A. V. S. Karunarathne
Date: 2018-08-29 00:00:00

Convolutional neural networks are able to perform a hierarchical learning
process starting with local features. However, a limited attention is paid to
enhancing such elementary level features like edges. We propose and evaluate
two wavelet-based edge feature enhancement methods to preprocess the input
images to convolutional neural networks. The first method develops feature
enhanced representations by decomposing the input images using wavelet
transform and limited reconstructing subsequently. The second method develops
such feature enhanced inputs to the network using local modulus maxima of
wavelet coefficients. For each method, we have developed a new preprocessing
layer by implementing each purposed method and have appended to the network
architecture. Our empirical evaluations demonstrate that the proposed methods
are outperforming the baselines and previously published work with significant
accuracy gains.
Interpretation of Natural Language Rules in Conversational Machine Reading
Authors: Marzieh Saeidi, Max Bartolo, Patrick Lewis, Sameer Singh, Tim Rocktäschel, Mike Sheldon, Guillaume Bouchard, Sebastian Riedel
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Question Answering, Reading Comprehension

Most work in machine reading focuses on question answering problems where the
answer is directly expressed in the text to read. However, many real-world
question answering problems require the reading of text not because it contains
the literal answer, but because it contains a recipe to derive an answer
together with the reader's background knowledge. One example is the task of
interpreting regulations to answer "Can I...?" or "Do I have to...?" questions
such as "I am working in Canada. Do I have to carry on paying UK National
Insurance?" after reading a UK government website about this topic. This task
requires both the interpretation of rules and the application of background
knowledge. It is further complicated due to the fact that, in practice, most
questions are underspecified, and a human assistant will regularly have to ask
clarification questions such as "How long have you been working abroad?" when
the answer cannot be directly derived from the question and text. In this
paper, we formalise this task and develop a crowd-sourcing strategy to collect
32k task instances based on real-world rules and crowd-generated questions and
scenarios. We analyse the challenges of this task and assess its difficulty by
evaluating the performance of rule-based and machine-learning baselines. We
observe promising results when no background knowledge is necessary, and
substantial room for improvement whenever background knowledge is needed.
Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks
Authors: Yurui Qu, Li Jing, Yichen Shen, Min Qiu, Marin Soljacic
Date: 2018-08-27 00:00:00
Tasks: Multi-Task Learning, Transfer Learning

Deep learning is known to be data-hungry, which hinders its application in many areas of science when datasets are small. Here, we propose to use transfer learning methods to migrate knowledge between different physical scenarios and significantly improve the prediction accuracy of artificial neural networks trained on a small dataset. This method can help reduce the demand for expensive data by making use of additional inexpensive data. First, we demonstrate that in predicting the transmission from multilayer photonic film, the relative error rate is reduced by 46.8% (26.5%) when the source data comes from 10-layer (8-layer) films and the target data comes from 8-layer (10-layer) films. Second, we show that the relative error rate is decreased by 22% when knowledge is transferred between two very different physical scenarios: transmission from multilayer films and scattering from multilayer nanoparticles. Finally, we propose a multi-task learning method to improve the performance of different physical scenarios simultaneously in which each task only has a small dataset.
Targeted Nonlinear Adversarial Perturbations in Images and Videos
Authors: Roberto Rey-de-Castro, Herschel Rabitz
Date: 2018-08-27 00:00:00
Tasks: Action Recognition, Data Augmentation, General Classification, Temporal Action Localization, Video Compression

We introduce a method for learning adversarial perturbations targeted to
individual images or videos. The learned perturbations are found to be sparse
while at the same time containing a high level of feature detail. Thus, the
extracted perturbations allow a form of object or action recognition and
provide insights into what features the studied deep neural network models
consider important when reaching their classification decisions. From an
adversarial point of view, the sparse perturbations successfully confused the
models into misclassifying, although the perturbed samples still belonged to
the same original class by visual examination. This is discussed in terms of a
prospective data augmentation scheme. The sparse yet high-quality perturbations
may also be leveraged for image or video compression.
Iterative multi-path tracking for video and volume segmentation with sparse point supervision
Authors: Laurent Lejeune, Jan Grossrieder, Raphael Sznitman
Date: 2018-08-27 00:00:00
Tasks: BIG-bench Machine Learning, Object, Segmentation

Recent machine learning strategies for segmentation tasks have shown great
ability when trained on large pixel-wise annotated image datasets. It remains a
major challenge however to aggregate such datasets, as the time and monetary
cost associated with collecting extensive annotations is extremely high. This
is particularly the case for generating precise pixel-wise annotations in video
and volumetric image data. To this end, this work presents a novel framework to
produce pixel-wise segmentations using minimal supervision. Our method relies
on 2D point supervision, whereby a single 2D location within an object of
interest is provided on each image of the data. Our method then estimates the
object appearance in a semi-supervised fashion by learning
object-image-specific features and by using these in a semi-supervised learning
framework. Our object model is then used in a graph-based optimization problem
that takes into account all provided locations and the image data in order to
infer the complete pixel-wise segmentation. In practice, we solve this
optimally as a tracking problem using a K-shortest path approach. Both the
object model and segmentation are then refined iteratively to further improve
the final segmentation. We show that by collecting 2D locations using a gaze
tracker, our approach can provide state-of-the-art segmentations on a range of
objects and image modalities (video and 3D volumes), and that these can then be
used to train supervised machine learning classifiers.
Task adapted reconstruction for inverse problems
Authors: Jonas Adler, Sebastian Lunz, Olivier Verdier, Carola-Bibiane Schönlieb, Ozan Öktem
Date: 2018-08-27 00:00:00
Tasks: Image Reconstruction

The paper considers the problem of performing a task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed
inverse problem. A key aspect is to formalize the steps of reconstruction and
task as appropriate estimators (non-randomized decision rules) in statistical
estimation problems. The implementation makes use of (deep) neural networks to
provide a differentiable parametrization of the family of estimators for both
steps. These networks are combined and jointly trained against suitable
supervised training data in order to minimize a joint differentiable loss
function, resulting in an end-to-end task adapted reconstruction method. The
suggested framework is generic, yet adaptable, with a plug-and-play structure
for adjusting both the inverse problem and the task at hand. More precisely,
the data model (forward operator and statistical model of the noise) associated
with the inverse problem is exchangeable, e.g., by using neural network
architecture given by a learned iterative method. Furthermore, any task that is
encodable as a trainable neural network can be used. The approach is
demonstrated on joint tomographic image reconstruction, classification and
joint tomographic image reconstruction segmentation.
A Deeper Insight into the UnDEMoN: Unsupervised Deep Network for Depth and Ego-Motion Estimation
Authors: Madhu Babu V, Anima Majumder, Kaushik Das, Swagat Kumar
Date: 2018-08-27 00:00:00
Tasks: Depth Estimation, Motion Estimation, Pose Estimation

This paper presents an unsupervised deep learning framework called UnDEMoN
for estimating dense depth map and 6-DoF camera pose information directly from
monocular images. The proposed network is trained using unlabeled monocular
stereo image pairs and is shown to provide superior performance in depth and
ego-motion estimation compared to the existing state-of-the-art. These
improvements are achieved by introducing a new objective function that aims to
minimize spatial as well as temporal reconstruction losses simultaneously.
These losses are defined using bi-linear sampling kernel and penalized using
the Charbonnier penalty function. The objective function, thus created,
provides robustness to image gradient noises thereby improving the overall
estimation accuracy without resorting to any coarse to fine strategies which
are currently prevalent in the literature. Another novelty lies in the fact
that we combine a disparity-based depth estimation network with a pose
estimation network to obtain absolute scale-aware 6 DOF Camera pose and
superior depth map. The effectiveness of the proposed approach is demonstrated
through performance comparison with the existing supervised and unsupervised
methods on the KITTI driving dataset.
Twin-GAN -- Unpaired Cross-Domain Image Translation with Weight-Sharing GANs
Authors: Jerry Li
Date: 2018-08-26 00:00:00
Tasks: Translation

We present a framework for translating unlabeled images from one domain into
analog images in another domain. We employ a progressively growing
skip-connected encoder-generator structure and train it with a GAN loss for
realistic output, a cycle consistency loss for maintaining same-domain
translation identity, and a semantic consistency loss that encourages the
network to keep the input semantic features in the output. We apply our
framework on the task of translating face images, and show that it is capable
of learning semantic mappings for face images with no supervised one-to-one
image mapping.
A MapReduce based Big-data Framework for Object Extraction from Mosaic Satellite Images
Authors: Suleyman Eken, Ahmet Sayar
Date: 2018-08-26 00:00:00
Tasks: Distributed Computing

We propose a framework stitching of vector representations of large scale
raster mosaic images in distributed computing model. In this way, the negative
effect of the lack of resources of the central system and scalability problem
can be eliminated. The product obtained by this study can be used in
applications requiring spatial and temporal analysis on big satellite map
images. This study also shows that big data frameworks are not only used in
applications of text-based data mining and machine learning algorithms, but
also used in applications of algorithms in image processing. The effectiveness
of the product realized with this project is also going to be proven by
scalability and performance tests performed on real world LandSat-8 satellite
images.
Blind Ptychography by Douglas-Rachford Splitting
Authors: A. Fannjiang, Z. Zhang
Date: 2018-08-26 00:00:00

Blind ptychography is the scanning version of coherent diffractive imaging
which seeks to recover both the object and the probe simultaneously. Based on
alternating minimization by Douglas-Rachford splitting, AMDRS is a blind
ptychographic algorithm informed by the uniqueness theory, the Poisson noise
model and the stability analysis. Enhanced by the initialization method and the
use of a randomly phased mask, AMDRS converges globally and geometrically.
Three boundary conditions are considered in the simulations: periodic,
dark-field and bright-field boundary conditions. The dark-field boundary
condition is suited for isolated objects while the bright-field boundary
condition is for non-isolated objects. The periodic boundary condition is a
mathematically convenient reference point. Depending on the avail- ability of
the boundary prior the dark-field and the bright-field boundary conditions may
or may not be enforced in the reconstruction. Not surprisingly, enforcing the
boundary condition improves the rate of convergence, sometimes in a significant
way. Enforcing the bright-field condition in the reconstruction can also remove
the linear phase ambiguity.
Road User Abnormal Trajectory Detection using a Deep Autoencoder
Authors: Pankaj Raj Roy, Guillaume-Alexandre Bilodeau
Date: 2018-08-25 00:00:00
Tasks: BIG-bench Machine Learning, Outlier Detection

In this paper, we focus on the development of a method that detects abnormal
trajectories of road users at traffic intersections. The main difficulty with
this is the fact that there are very few abnormal data and the normal ones are
insufficient for the training of any kinds of machine learning model. To tackle
these problems, we proposed the solution of using a deep autoencoder network
trained solely through augmented data considered as normal. By generating
artificial abnormal trajectories, our method is tested on four different
outdoor urban users scenes and performs better compared to some classical
outlier detection methods.
MSCE: An edge preserving robust loss function for improving super-resolution algorithms
Authors: Ram Krishna Pandey, Nabagata Saha, Samarjit Karmakar, A. G. Ramakrishnan
Date: 2018-08-25 00:00:00
Tasks: Deep Learning, SSIM, Super-Resolution

With the recent advancement in the deep learning technologies such as CNNs
and GANs, there is significant improvement in the quality of the images
reconstructed by deep learning based super-resolution (SR) techniques. In this
work, we propose a robust loss function based on the preservation of edges
obtained by the Canny operator. This loss function, when combined with the
existing loss function such as mean square error (MSE), gives better SR
reconstruction measured in terms of PSNR and SSIM. Our proposed loss function
guarantees improved performance on any existing algorithm using MSE loss
function, without any increase in the computational complexity during testing.
Organ at Risk Segmentation in Head and Neck CT Images by Using a Two-Stage Segmentation Framework Based on 3D U-Net
Authors: Yueyue Wang, Liang Zhao, Zhijian Song, Manning Wang
Date: 2018-08-25 00:00:00
Tasks: Segmentation

Accurate segmentation of organ at risk (OAR) play a critical role in the treatment planning of image guided radiation treatment of head and neck cancer. This segmentation task is challenging for both human and automatic algorithms because of the relatively large number of OARs to be segmented, the large variability of the size and morphology across different OARs, and the low contrast of between some OARs and the background. In this paper, we proposed a two-stage segmentation framework based on 3D U-Net. In this framework, the segmentation of each OAR is decomposed into two sub-tasks: locating a bounding box of the OAR and segment it from a small volume within the bounding box, and each sub-tasks is fulfilled by a dedicated 3D U-Net. The decomposition makes each of the two sub-tasks much easier, so that they can be better completed. We evaluated the proposed method and compared it to state-of-the-art methods by using the MICCAI 2015 Challenge dataset. In terms of the boundary-based metric 95HD, the proposed method ranked first in eight of all nine OARs and ranked second in the other OAR. In terms of the area-based metric DSC, the proposed method ranked first in six of the nine OARs and ranked second in the other three OARs with small difference with the first one.
Deep Learning Based Vehicle Make-Model Classification
Authors: Burak Satar, Ahmet Emir Dirik
Date: 2018-08-23 00:00:00
Tasks: Classification, Deep Learning, General Classification, model

This paper studies the problems of vehicle make & model classification. Some
of the main challenges are reaching high classification accuracy and reducing
the annotation time of the images. To address these problems, we have created a
fine-grained database using online vehicle marketplaces of Turkey. A pipeline
is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN
(Convolutional Neural Network) model to train on the database. In the pipeline,
we first detect the vehicles by following an algorithm which reduces the time
for annotation. Then, we feed them into the CNN model. It is reached
approximately 4% better classification accuracy result than using a
conventional CNN model. Next, we propose to use the detected vehicles as ground
truth bounding box (GTBB) of the images and feed them into an SSD model in
another pipeline. At this stage, it is reached reasonable classification
accuracy result without using perfectly shaped GTBB. Lastly, an application is
implemented in a use case by using our proposed pipelines. It detects the
unauthorized vehicles by comparing their license plate numbers and make &
models. It is assumed that license plates are readable.
The Sloan Digital Sky Survey-II Supernova Survey: Search Algorithm and Follow-up Observations
Authors: Masao Sako, B. Bassett, A. Becker, D. Cinabro, F. DeJongh, D. L. Depoy, B. Dilday, M. Doi, J. A. Frieman, P. M. Garnavich, C. J. Hogan, J. Holtzman, S. Jha, R. Kessler, K. Konishi, H. Lampeitl, J. Marriner, G. Miknaitis, R. C. Nichol, J. L. Prieto, A. G. Riess, M. W. Richmond, R. Romani, D. P. Schneider, M. Smith, M. SubbaRao, N. Takanashi, K. Tokita, K. van der Heyden, N. Yasuda, C. Zheng, J. Barentine, H. Brewington, C. Choi, J. Dembicky, M. Harnavek, Y. Ihara, M. Im, W. Ketzeback, S. J. Kleinman, J. Krzesiński, D. C. Long, E. Malanushenko, V. Malanushenko, R. J. McMillan, T. Morokuma, A. Nitta, K. Pan, G. Saurage, S. A. Snedden
Date: 2007-08-21 00:00:00
Tasks: Survey

The Sloan Digital Sky Survey-II Supernova Survey has identified a large
number of new transient sources in a 300 sq. deg. region along the celestial
equator during its first two seasons of a three-season campaign. Multi-band
(ugriz) light curves were measured for most of the sources, which include solar
system objects, Galactic variable stars, active galactic nuclei, supernovae
(SNe), and other astronomical transients. The imaging survey is augmented by an
extensive spectroscopic follow-up program to identify SNe, measure their
redshifts, and study the physical conditions of the explosions and their
environment through spectroscopic diagnostics. During the survey, light curves
are rapidly evaluated to provide an initial photometric type of the SNe, and a
selected sample of sources are targeted for spectroscopic observations. In the
first two seasons, 476 sources were selected for spectroscopic observations, of
which 403 were identified as SNe. For the Type Ia SNe, the main driver for the
Survey, our photometric typing and targeting efficiency is 90%. Only 6% of the
photometric SN Ia candidates were spectroscopically classified as non-SN Ia
instead, and the remaining 4% resulted in low signal-to-noise, unclassified
spectra. This paper describes the search algorithm and the software, and the
real-time processing of the SDSS imaging data. We also present the details of
the supernova candidate selection procedures and strategies for follow-up
spectroscopic and imaging observations of the discovered sources.
Advanced Bloom Filter Based Algorithms for Efficient Approximate Data De-Duplication in Streams
Authors: Suman K. Bera, Sourav Dutta, Ankur Narang, Souvik Bhattacherjee
Date: 2012-12-17 00:00:00
Tasks: Management

Applications involving telecommunication call data records, web pages, online
transactions, medical records, stock markets, climate warning systems, etc.,
necessitate efficient management and processing of such massively exponential
amount of data from diverse sources. De-duplication or Intelligent Compression
in streaming scenarios for approximate identification and elimination of
duplicates from such unbounded data stream is a greater challenge given the
real-time nature of data arrival. Stable Bloom Filters (SBF) addresses this
problem to a certain extent. .
  In this work, we present several novel algorithms for the problem of
approximate detection of duplicates in data streams. We propose the Reservoir
Sampling based Bloom Filter (RSBF) combining the working principle of reservoir
sampling and Bloom Filters. We also present variants of the novel Biased
Sampling based Bloom Filter (BSBF) based on biased sampling concepts. We also
propose a randomized load balanced variant of the sampling Bloom Filter
approach to efficiently tackle the duplicate detection. In this work, we thus
provide a generic framework for de-duplication using Bloom Filters. Using
detailed theoretical analysis we prove analytical bounds on the false positive
rate, false negative rate and convergence rate of the proposed structures. We
exhibit that our models clearly outperform the existing methods. We also
demonstrate empirical analysis of the structures using real-world datasets (3
million records) and also with synthetic datasets (1 billion records) capturing
various input distributions.