pwc-archive/papers-with-abstracts huggingface dataset:
Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal Learning
Authors: Tom Tirer, Raja Giryes
Date: 2018-11-30 00:00:00
Tasks: Denoising, Image Super-Resolution, Super-Resolution

While deep neural networks exhibit state-of-the-art results in the task of image super-resolution (SR) with a fixed known acquisition process (e.g., a bicubic downscaling kernel), they experience a huge performance loss when the real observation model mismatches the one used in training. Recently, two different techniques suggested to mitigate this deficiency, i.e., enjoy the advantages of deep learning without being restricted by the training phase. The first one follows the plug-and-play (P&P) approach that solves general inverse problems (e.g., SR) by using Gaussian denoisers for handling the prior term in model-based optimization schemes. The second builds on internal recurrence of information inside a single image, and trains a super-resolver network at test time on examples synthesized from the low-resolution image. Our work incorporates these two independent strategies, enjoying the impressive generalization capabilities of deep learning, captured by the first, and further improving it through internal learning at test time. First, we apply a recent P&P strategy to SR. Then, we show how it may become image-adaptive in test time. This technique outperforms the above two strategies on popular datasets and gives better results than other state-of-the-art methods in practical cases where the observation model is inexact or unknown in advance.
Lipizzaner: A System That Scales Robust Generative Adversarial Network Training
Authors: Tom Schmiedlechner, Ignavier Ng Zhi Yong, Abdullah Al-Dujaili, Erik Hemberg, Una-May O'Reilly
Date: 2018-11-30 00:00:00
Tasks: Generative Adversarial Network

GANs are difficult to train due to convergence pathologies such as mode and
discriminator collapse. We introduce Lipizzaner, an open source software system
that allows machine learning engineers to train GANs in a distributed and
robust way. Lipizzaner distributes a competitive coevolutionary algorithm
which, by virtue of dual, adapting, generator and discriminator populations, is
robust to collapses. The algorithm is well suited to efficient distribution
because it uses a spatial grid abstraction. Training is local to each cell and
strong intermediate training results are exchanged among overlapping
neighborhoods allowing high performing solutions to propagate and improve with
more rounds of training. Experiments on common image datasets overcome critical
collapses. Communication overhead scales linearly when increasing the number of
compute instances and we observe that increasing scale leads to improved model
performance.
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Authors: Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, Patrick Pérez
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Image-to-Image Translation, Segmentation, Semantic Segmentation, Synthetic-to-Real Translation, Unsupervised Domain Adaptation

Semantic segmentation is a key problem for many computer vision tasks. While
approaches based on convolutional neural networks constantly break new records
on different benchmarks, generalizing well to diverse testing environments
remains a major challenge. In numerous real world applications, there is indeed
a large gap between data distributions in train and test domains, which results
in severe performance loss at run-time. In this work, we address the task of
unsupervised domain adaptation in semantic segmentation with losses based on
the entropy of the pixel-wise predictions. To this end, we propose two novel,
complementary methods using (i) entropy loss and (ii) adversarial loss
respectively. We demonstrate state-of-the-art performance in semantic
segmentation on two challenging "synthetic-2-real" set-ups and show that the
approach can also be used for detection.
Beltrami-Net: Domain Independent Deep D-bar Learning for Absolute Imaging with Electrical Impedance Tomography (a-EIT)
Authors: S. J. Hamilton, A. Hänninen, A. Hauptmann, V. Kolehmainen
Date: 2018-11-30 00:00:00
Tasks: Image Reconstruction

Objective: To develop, and demonstrate the feasibility of, a novel image
reconstruction method for absolute Electrical Impedance Tomography (a-EIT) that
pairs deep learning techniques with real-time robust D-bar methods. Approach: A
D-bar method is paired with a trained Convolutional Neural Network (CNN) as a
post-processing step. Training data is simulated for the network using no
knowledge of the boundary shape by using an associated nonphysical Beltrami
equation rather than simulating the traditional current and voltage data
specific to a given domain. This allows the training data to be boundary shape
independent. The method is tested on experimental data from two EIT systems
(ACT4 and KIT4). Main Results: Post processing the D-bar images with a CNN
produces significant improvements in image quality measured by Structural
SIMilarity indices (SSIMs) as well as relative $\ell_2$ and $\ell_1$ image
errors. Significance: This work demonstrates that more general networks can be
trained without being specific about boundary shape, a key challenge in EIT
image reconstruction. The work is promising for future studies involving
databases of anatomical atlases.
Runtime Analysis for Self-adaptive Mutation Rates
Authors: Benjamin Doerr, Carsten Witt, Jing Yang
Date: 2018-11-30 00:00:00

We propose and analyze a self-adaptive version of the $(1,\lambda)$
evolutionary algorithm in which the current mutation rate is part of the
individual and thus also subject to mutation. A rigorous runtime analysis on
the OneMax benchmark function reveals that a simple local mutation scheme for
the rate leads to an expected optimization time (number of fitness evaluations)
of $O(n\lambda/\log\lambda+n\log n)$ when $\lambda$ is at least $C \ln n$ for
some constant $C > 0$. For all values of $\lambda \ge C \ln n$, this
performance is asymptotically best possible among all $\lambda$-parallel
mutation-based unbiased black-box algorithms.
  Our result shows that self-adaptation in evolutionary computation can find
complex optimal parameter settings on the fly. At the same time, it proves that
a relatively complicated self-adjusting scheme for the mutation rate proposed
by Doerr, Gie{\ss}en, Witt, and Yang~(GECCO~2017) can be replaced by our simple
endogenous scheme.
  On the technical side, the paper contributes new tools for the analysis of
two-dimensional drift processes arising in the analysis of dynamic parameter
choices in EAs, including bounds on occupation probabilities in processes with
non-constant drift.
Practical Full Resolution Learned Lossless Image Compression
Authors: Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc van Gool
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Compression

We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000. At the core of our method is a fully parallelizable hierarchical probabilistic model for adaptive entropy coding which is optimized end-to-end for the compression task. In contrast to recent autoregressive discrete probabilistic models such as PixelCNN, our method i) models the image distribution jointly with learned auxiliary representations instead of exclusively modeling the image distribution in RGB space, and ii) only requires three forward-passes to predict all pixel probabilities instead of one for each pixel. As a result, L3C obtains over two orders of magnitude speedups when sampling compared to the fastest PixelCNN variant (Multiscale-PixelCNN). Furthermore, we find that learning the auxiliary representation is crucial and outperforms predefined auxiliary representations such as an RGB pyramid significantly.
Graph-Based Global Reasoning Networks
Authors: Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Classification, Action Recognition, image-classification, Image Classification, Relational Reasoning, Semantic Segmentation, Temporal Action Localization

Globally modeling and reasoning over relations between regions can be
beneficial for many computer vision tasks on both images and videos.
Convolutional Neural Networks (CNNs) excel at modeling local relations by
convolution operations, but they are typically inefficient at capturing global
relations between distant regions and require stacking multiple convolution
layers. In this work, we propose a new approach for reasoning globally in which
a set of features are globally aggregated over the coordinate space and then
projected to an interaction space where relational reasoning can be efficiently
computed. After reasoning, relation-aware features are distributed back to the
original coordinate space for down-stream tasks. We further present a highly
efficient instantiation of the proposed approach and introduce the Global
Reasoning unit (GloRe unit) that implements the coordinate-interaction space
mapping by weighted global pooling and weighted broadcasting, and the relation
reasoning via graph convolution on a small graph in interaction space. The
proposed GloRe unit is lightweight, end-to-end trainable and can be easily
plugged into existing CNNs for a wide range of tasks. Extensive experiments
show our GloRe unit can consistently boost the performance of state-of-the-art
backbone architectures, including ResNet, ResNeXt, SE-Net and DPN, for both 2D
and 3D CNNs, on image classification, semantic segmentation and video action
recognition task.
Real Time Bangladeshi Sign Language Detection using Faster R-CNN
Authors: Oishee Bintey Hoque, Mohammad Imrul Jubair, Md. Saiful Islam, Al-Farabi Akash, Alvin Sachie Paulson
Date: 2018-11-30 00:00:00
Tasks: object-detection, Object Detection

Bangladeshi Sign Language (BdSL) is a commonly used medium of communication
for the hearing-impaired people in Bangladesh. Developing a real time system to
detect these signs from images is a great challenge. In this paper, we present
a technique to detect BdSL from images that performs in real time. Our method
uses Convolutional Neural Network based object detection technique to detect
the presence of signs in the image region and to recognize its class. For this
purpose, we adopted Faster Region-based Convolutional Network approach and
developed a dataset $-$ BdSLImset $-$ to train our system. Previous research
works in detecting BdSL generally depend on external devices while most of the
other vision-based techniques do not perform efficiently in real time. Our
approach, however, is free from such limitations and the experimental results
demonstrate that the proposed method successfully identifies and recognizes
Bangladeshi signs in real time.
Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed Low-Rank Matrices
Authors: Yuxin Chen, Chen Cheng, Jianqing Fan
Date: 2018-11-30 00:00:00

This paper is concerned with the interplay between statistical asymmetry and spectral methods. Suppose we are interested in estimating a rank-1 and symmetric matrix $\mathbf{M}^{\star}\in \mathbb{R}^{n\times n}$, yet only a randomly perturbed version $\mathbf{M}$ is observed. The noise matrix $\mathbf{M}-\mathbf{M}^{\star}$ is composed of zero-mean independent (but not necessarily homoscedastic) entries and is, therefore, not symmetric in general. This might arise, for example, when we have two independent samples for each entry of $\mathbf{M}^{\star}$ and arrange them into an {\em asymmetric} data matrix $\mathbf{M}$. The aim is to estimate the leading eigenvalue and eigenvector of $\mathbf{M}^{\star}$. We demonstrate that the leading eigenvalue of the data matrix $\mathbf{M}$ can be $O(\sqrt{n})$ times more accurate --- up to some log factor --- than its (unadjusted) leading singular value in eigenvalue estimation. Further, the perturbation of any linear form of the leading eigenvector of $\mathbf{M}$ --- say, entrywise eigenvector perturbation --- is provably well-controlled. This eigen-decomposition approach is fully adaptive to heteroscedasticity of noise without the need of careful bias correction or any prior knowledge about the noise variance. We also provide partial theory for the more general rank-$r$ case. The takeaway message is this: arranging the data samples in an asymmetric manner and performing eigen-decomposition could sometimes be beneficial.
Generative Models for Simulating Mobility Trajectories
Authors: Vaibhav Kulkarni, Natasa Tagasovska, Thibault Vatter, Benoit Garbinato
Date: 2018-11-30 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Mobility datasets are fundamental for evaluating algorithms pertaining to
geographic information systems and facilitating experimental reproducibility.
But privacy implications restrict sharing such datasets, as even aggregated
location-data is vulnerable to membership inference attacks. Current synthetic
mobility dataset generators attempt to superficially match a priori modeled
mobility characteristics which do not accurately reflect the real-world
characteristics. Modeling human mobility to generate synthetic yet semantically
and statistically realistic trajectories is therefore crucial for publishing
trajectory datasets having satisfactory utility level while preserving user
privacy. Specifically, long-range dependencies inherent to human mobility are
challenging to capture with both discriminative and generative models. In this
paper, we benchmark the performance of recurrent neural architectures (RNNs),
generative adversarial networks (GANs) and nonparametric copulas to generate
synthetic mobility traces. We evaluate the generated trajectories with respect
to their geographic and semantic similarity, circadian rhythms, long-range
dependencies, training and generation time. We also include two sample tests to
assess statistical similarity between the observed and simulated distributions,
and we analyze the privacy tradeoffs with respect to membership inference and
location-sequence attacks.
Structure and Motion from Multiframes
Authors: Mieczysław A. Kłopotek
Date: 2018-11-30 00:00:00

The paper gives an overview of the problems and methods of recovery of
structure and motion parameters of rigid bodies from multiframes.
TIFTI: A Framework for Extracting Drug Intervals from Longitudinal Clinic Notes
Authors: Monica Agrawal, Griffin Adams, Nathan Nussbaum, Benjamin Birnbaum
Date: 2018-11-30 00:00:00

Oral drugs are becoming increasingly common in oncology care. In contrast to
intravenous chemotherapy, which is administered in the clinic and carefully
tracked via structure electronic health records (EHRs), oral drug treatment is
self-administered and therefore not tracked as well. Often, the details of oral
cancer treatment occur only in unstructured clinic notes. Extracting this
information is critical to understanding a patient's treatment history. Yet,
this a challenging task because treatment intervals must be inferred
longitudinally from both explicit mentions in the text as well as from document
timestamps. In this work, we present TIFTI (Temporally Integrated Framework for
Treatment Intervals), a robust framework for extracting oral drug treatment
intervals from a patient's unstructured notes. TIFTI leverages distinct sources
of temporal information by breaking the problem down into two separate
subtasks: document-level sequence labeling and date extraction. On a labeled
dataset of metastatic renal-cell carcinoma (RCC) patients, it exactly matched
the labeled start date in 46% of the examples (86% of the examples within 30
days), and it exactly matched the labeled end date in 52% of the examples (78%
of the examples within 30 days). Without retraining, the model achieved a
similar level of performance on a labeled dataset of advanced non-small-cell
lung cancer (NSCLC) patients.
iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
Authors: Guilherme Aresta, Colin Jacobs, Teresa Araújo, António Cunha, Isabel Ramos, Bram van Ginneken, Aurélio Campilho
Date: 2018-11-30 00:00:00
Tasks: Interactive Segmentation, Lung Nodule Segmentation, Segmentation

We propose iW-Net, a deep learning model that allows for both automatic and
interactive segmentation of lung nodules in computed tomography images. iW-Net
is composed of two blocks: the first one provides an automatic segmentation and
the second one allows to correct it by analyzing 2 points introduced by the
user in the nodule's boundary. For this purpose, a physics inspired weight map
that takes the user input into account is proposed, which is used both as a
feature map and in the system's loss function. Our approach is extensively
evaluated on the public LIDC-IDRI dataset, where we achieve a state-of-the-art
performance of 0.55 intersection over union vs the 0.59 inter-observer
agreement. Also, we show that iW-Net allows to correct the segmentation of
small nodules, essential for proper patient referral decision, as well as
improve the segmentation of the challenging non-solid nodules and thus may be
an important tool for increasing the early diagnosis of lung cancer.
A Tutorial for Weighted Bipolar Argumentation with Continuous Dynamical Systems and the Java Library Attractor
Authors: Nico Potyka
Date: 2018-11-30 00:00:00

Weighted bipolar argumentation frameworks allow modeling decision problems
and online discussions by defining arguments and their relationships. The
strength of arguments can be computed based on an initial weight and the
strength of attacking and supporting arguments. While previous approaches
assumed an acyclic argumentation graph and successively set arguments' strength
based on the strength of their parents, recently continuous dynamical systems
have been proposed as an alternative. Continuous models update arguments'
strength simultaneously and continuously. While there are currently no
analytical guarantees for convergence in general graphs, experiments show that
continuous models can converge quickly in large cyclic graphs with thousands of
arguments. Here, we focus on the high-level ideas of this approach and explain
key results and applications. We also introduce Attractor, a Java library that
can be used to solve weighted bipolar argumentation problems. Attractor
contains implementations of several discrete and continuous models and
numerical algorithms to compute solutions. It also provides base classes that
can be used to implement, to evaluate and to compare continuous models easily.
TextMountain: Accurate Scene Text Detection via Instance Segmentation
Authors: Yixing Zhu, Jun Du
Date: 2018-11-30 00:00:00
Tasks: Binary Classification, Instance Segmentation, Scene Text Detection, Semantic Segmentation, Text Detection

In this paper, we propose a novel scene text detection method named
TextMountain. The key idea of TextMountain is making full use of border-center
information. Different from previous works that treat center-border as a binary
classification problem, we predict text center-border probability (TCBP) and
text center-direction (TCD). The TCBP is just like a mountain whose top is text
center and foot is text border. The mountaintop can separate text instances
which cannot be easily achieved using semantic segmentation map and its rising
direction can plan a road to top for each pixel on mountain foot at the group
stage. The TCD helps TCBP learning better. Our label rules will not lead to the
ambiguous problem with the transformation of angle, so the proposed method is
robust to multi-oriented text and can also handle well with curved text. In
inference stage, each pixel at the mountain foot needs to search the path to
the mountaintop and this process can be efficiently completed in parallel,
yielding the efficiency of our method compared with others. The experiments on
MLT, ICDAR2015, RCTW-17 and SCUT-CTW1500 databases demonstrate that the
proposed method achieves better or comparable performance in terms of both
accuracy and efficiency. It is worth mentioning our method achieves an
F-measure of 76.85% on MLT which outperforms the previous methods by a large
margin. Code will be made available.
The GAN that Warped: Semantic Attribute Editing with Unpaired Data
Authors: Garoe Dorta, Sara Vicente, Neill D. F. Campbell, Ivor J. A. Simpson
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2020 6
Tasks: 4k, Attribute

Deep neural networks have recently been used to edit images with great success, in particular for faces. However, they are often limited to only being able to work at a restricted range of resolutions. Many methods are so flexible that face edits can often result in an unwanted loss of identity. This work proposes to learn how to perform semantic image edits through the application of smooth warp fields. Previous approaches that attempted to use warping for semantic edits required paired data, i.e. example images of the same subject with different semantic attributes. In contrast, we employ recent advances in Generative Adversarial Networks that allow our model to be trained with unpaired data. We demonstrate face editing at very high resolutions (4k images) with a single forward pass of a deep network at a lower resolution. We also show that our edits are substantially better at preserving the subject's identity. The robustness of our approach is demonstrated by showing plausible image editing results on the Cub200 birds dataset. To our knowledge this has not been previously accomplished, due the challenging nature of the dataset.
Measure, Manifold, Learning, and Optimization: A Theory Of Neural Networks
Authors: Shuai Li
Date: 2018-11-30 00:00:00

We present a formal measure-theoretical theory of neural networks (NN) built
on probability coupling theory. Our main contributions are summarized as
follows.
  * Built on the formalism of probability coupling theory, we derive an
algorithm framework, named Hierarchical Measure Group and Approximate System
(HMGAS), nicknamed S-System, that is designed to learn the complex
hierarchical, statistical dependency in the physical world.
  * We show that NNs are special cases of S-System when the probability kernels
assume certain exponential family distributions. Activation Functions are
derived formally. We further endow geometry on NNs through information
geometry, show that intermediate feature spaces of NNs are stochastic
manifolds, and prove that "distance" between samples is contracted as layers
stack up.
  * S-System shows NNs are inherently stochastic, and under a set of realistic
boundedness and diversity conditions, it enables us to prove that for large
size nonlinear deep NNs with a class of losses, including the hinge loss, all
local minima are global minima with zero loss errors, and regions around the
minima are flat basins where all eigenvalues of Hessians are concentrated
around zero, using tools and ideas from mean field theory, random matrix
theory, and nonlinear operator equations.
  * S-System, the information-geometry structure and the optimization behaviors
combined completes the analog between Renormalization Group (RG) and NNs. It
shows that a NN is a complex adaptive system that estimates the statistic
dependency of microscopic object, e.g., pixels, in multiple scales. Unlike
clear-cut physical quantity produced by RG in physics, e.g., temperature, NNs
renormalize/recompose manifolds emerging through learning/optimization that
divide the sample space into highly semantically meaningful groups that are
dictated by supervised labels (in supervised NNs).