pwc-archive/papers-with-abstracts huggingface dataset:
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
Authors: Amélie Royer, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, Kevin Murphy
Date: 2017-11-14 00:00:00
Proceeding: ICLR 2018 1
Tasks: Domain Adaptation, Image-to-Image Translation, Style Transfer, Translation, Unsupervised Image-To-Image Translation

Style transfer usually refers to the task of applying color and texture
information from a specific style image to a given content image while
preserving the structure of the latter. Here we tackle the more generic problem
of semantic style transfer: given two unpaired collections of images, we aim to
learn a mapping between the corpus-level style of each collection, while
preserving semantic content shared across the two domains. We introduce XGAN
("Cross-GAN"), a dual adversarial autoencoder, which captures a shared
representation of the common domain semantic content in an unsupervised way,
while jointly learning the domain-to-domain image translations in both
directions. We exploit ideas from the domain adaptation literature and define a
semantic consistency loss which encourages the model to preserve semantics in
the learned embedding space. We report promising qualitative results for the
task of face-to-cartoon translation. The cartoon dataset, CartoonSet, we
collected for this purpose is publicly available at
google.github.io/cartoonset/ as a new benchmark for semantic style transfer.
Fair Division Under Cardinality Constraints
Authors: Siddharth Barman, Arpita Biswas
Date: 2018-04-25 00:00:00
Tasks: Fairness

We consider the problem of fairly allocating indivisible goods, among agents, under cardinality constraints and additive valuations. In this setting, we are given a partition of the entire set of goods---i.e., the goods are categorized---and a limit is specified on the number of goods that can be allocated from each category to any agent. The objective here is to find a fair allocation in which the subset of goods assigned to any agent satisfies the given cardinality constraints. This problem naturally captures a number of resource-allocation applications, and is a generalization of the well-studied (unconstrained) fair division problem. The two central notions of fairness, in the context of fair division of indivisible goods, are envy freeness up to one good (EF1) and the (approximate) maximin share guarantee (MMS). We show that the existence and algorithmic guarantees established for these solution concepts in the unconstrained setting can essentially be achieved under cardinality constraints. Specifically, we develop efficient algorithms which compute EF1 and approximately MMS allocations in the constrained setting. Furthermore, focusing on the case wherein all the agents have the same additive valuation, we establish that EF1 allocations exist and can be computed efficiently even under laminar matroid constraints.
Unsupervised Disentangled Representation Learning with Analogical Relations
Authors: Zejian Li, Yongchuan Tang, Yongxing He
Date: 2018-04-25 00:00:00
Tasks: Disentanglement, Relation, Representation Learning

Learning the disentangled representation of interpretable generative factors
of data is one of the foundations to allow artificial intelligence to think
like people. In this paper, we propose the analogical training strategy for the
unsupervised disentangled representation learning in generative models. The
analogy is one of the typical cognitive processes, and our proposed strategy is
based on the observation that sample pairs in which one is different from the
other in one specific generative factor show the same analogical relation.
Thus, the generator is trained to generate sample pairs from which a designed
classifier can identify the underlying analogical relation. In addition, we
propose a disentanglement metric called the subspace score, which is inspired
by subspace learning methods and does not require supervised information.
Experiments show that our proposed training strategy allows the generative
models to find the disentangled factors, and that our methods can give
competitive performances as compared with the state-of-the-art methods.
Estimation with Low-Rank Time-Frequency Synthesis Models
Authors: Cédric Févotte, Matthieu Kowalski
Date: 2018-04-25 00:00:00
Tasks: Audio Signal Processing, Compressive Sensing

Many state-of-the-art signal decomposition techniques rely on a low-rank
factorization of a time-frequency (t-f) transform. In particular, nonnegative
matrix factorization (NMF) of the spectrogram has been considered in many audio
applications. This is an analysis approach in the sense that the factorization
is applied to the squared magnitude of the analysis coefficients returned by
the t-f transform. In this paper we instead propose a synthesis approach, where
low-rankness is imposed to the synthesis coefficients of the data signal over a
given t-f dictionary (such as a Gabor frame). As such we offer a novel modeling
paradigm that bridges t-f synthesis modeling and traditional analysis-based NMF
approaches. The proposed generative model allows in turn to design more
sophisticated multi-layer representations that can efficiently capture diverse
forms of structure. Additionally, the generative modeling allows to exploit t-f
low-rankness for compressive sensing. We present efficient iterative shrinkage
algorithms to perform estimation in the proposed models and illustrate the
capabilities of the new modeling paradigm over audio signal processing
examples.
RIFT: Multi-modal Image Matching Based on Radiation-invariant Feature Transform
Authors: Jiayuan Li, Qingwu Hu, Mingyao Ai
Date: 2018-04-25 00:00:00

Traditional feature matching methods such as scale-invariant feature
transform (SIFT) usually use image intensity or gradient information to detect
and describe feature points; however, both intensity and gradient are sensitive
to nonlinear radiation distortions (NRD). To solve the problem, this paper
proposes a novel feature matching algorithm that is robust to large NRD. The
proposed method is called radiation-invariant feature transform (RIFT). There
are three main contributions in RIFT: first, RIFT uses phase congruency (PC)
instead of image intensity for feature point detection. RIFT considers both the
number and repeatability of feature points, and detects both corner points and
edge points on the PC map. Second, RIFT originally proposes a maximum index map
(MIM) for feature description. MIM is constructed from the log-Gabor
convolution sequence and is much more robust to NRD than traditional gradient
map. Thus, RIFT not only largely improves the stability of feature detection,
but also overcomes the limitation of gradient information for feature
description. Third, RIFT analyzes the inherent influence of rotations on the
values of MIM, and realizes rotation invariance. We use six different types of
multi-model image datasets to evaluate RIFT, including optical-optical,
infrared-optical, synthetic aperture radar (SAR)-optical, depth-optical,
map-optical, and day-night datasets. Experimental results show that RIFT is
much more superior to SIFT and SAR-SIFT. To the best of our knowledge, RIFT is
the first feature matching algorithm that can achieve good performance on all
the above-mentioned types of multi-model images. The source code of RIFT and
multi-modal remote sensing image datasets are made public .
Semi-Supervised and Active Few-Shot Learning with Prototypical Networks
Authors: Rinu Boney, Alexander Ilin
Date: 2017-11-29 00:00:00
Tasks: Clustering, Few-Shot Learning, General Classification

We consider the problem of semi-supervised few-shot classification where a
classifier needs to adapt to new tasks using a few labeled examples and
(potentially many) unlabeled examples. We propose a clustering approach to the
problem. The features extracted with Prototypical Networks are clustered using
$K$-means with the few labeled examples guiding the clustering process. We note
that in many real-world applications the adaptation performance can be
significantly improved by requesting the few labels through user feedback. We
demonstrate good performance of the active adaptation strategy using image
data.
Stratified Negation in Limit Datalog Programs
Authors: Mark Kaminski, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik, Ian Horrocks
Date: 2018-04-25 00:00:00
Tasks: Negation

There has recently been an increasing interest in declarative data analysis,
where analytic tasks are specified using a logical language, and their
implementation and optimisation are delegated to a general-purpose query
engine. Existing declarative languages for data analysis can be formalised as
variants of logic programming equipped with arithmetic function symbols and/or
aggregation, and are typically undecidable. In prior work, the language of
$\mathit{limit\ programs}$ was proposed, which is sufficiently powerful to
capture many analysis tasks and has decidable entailment problem. Rules in this
language, however, do not allow for negation. In this paper, we study an
extension of limit programs with stratified negation-as-failure. We show that
the additional expressive power makes reasoning computationally more demanding,
and provide tight data complexity bounds. We also identify a fragment with
tractable data complexity and sufficient expressivity to capture many relevant
tasks.
Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D
Authors: Allan Grønlund, Kasper Green Larsen, Alexander Mathiasen, Jesper Sindahl Nielsen, Stefan Schneider, Mingzhou Song
Date: 2017-01-25 00:00:00
Tasks: Clustering

The $k$-Means clustering problem on $n$ points is NP-Hard for any dimension
$d\ge 2$, however, for the 1D case there exists exact polynomial time
algorithms. Previous literature reported an $O(kn^2)$ time dynamic programming
algorithm that uses $O(kn)$ space. It turns out that the problem has been
considered under a different name more than twenty years ago. We present all
the existing work that had been overlooked and compare the various solutions
theoretically. Moreover, we show how to reduce the space usage for some of
them, as well as generalize them to data structures that can quickly report an
optimal $k$-Means clustering for any $k$. Finally we also generalize all the
algorithms to work for the absolute distance and to work for any Bregman
Divergence. We complement our theoretical contributions by experiments that
compare the practical performance of the various algorithms.
Zigzag Learning for Weakly Supervised Object Detection
Authors: Xiaopeng Zhang, Jiashi Feng, Hongkai Xiong, Qi Tian
Date: 2018-04-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, object-detection, Object Detection, Weakly Supervised Object Detection

This paper addresses weakly supervised object detection with only image-level
supervision at training stage. Previous approaches train detection models with
entire images all at once, making the models prone to being trapped in
sub-optimums due to the introduced false positive examples. Unlike them, we
propose a zigzag learning strategy to simultaneously discover reliable object
instances and prevent the model from overfitting initial seeds. Towards this
goal, we first develop a criterion named mean Energy Accumulation Scores (mEAS)
to automatically measure and rank localization difficulty of an image
containing the target object, and accordingly learn the detector progressively
by feeding examples with increasing difficulty. In this way, the model can be
well prepared by training on easy examples for learning from more difficult
ones and thus gain a stronger detection ability more efficiently. Furthermore,
we introduce a novel masking regularization strategy over the high level
convolutional feature maps to avoid overfitting initial samples. These two
modules formulate a zigzag learning process, where progressive learning
endeavors to discover reliable object instances, and masking regularization
increases the difficulty of finding object instances properly. We achieve 47.6%
mAP on PASCAL VOC 2007, surpassing the state-of-the-arts by a large margin.
Intelligent Physiotherapy Through Procedural Content Generation
Authors: Shabnam Sadeghi Esfahlani, Tommy Thompson
Date: 2018-04-25 00:00:00

This paper describes an avenue for artificial and computational intelligence
techniques applied within games research to be deployed for purposes of
physical therapy. We provide an overview of prototypical research focussed on
the application of motion sensor input devices and virtual reality equipment
for rehabilitation of motor impairment an issue typical of patient's of
traumatic brain injuries. We highlight how advances in procedural content
generation and player modelling can stimulate development in this area by
improving quality of rehabilitation programmes and measuring patient
performance.
Structured Pruning for Efficient ConvNets via Incremental Regularization
Authors: Huan Wang, Qiming Zhang, Yuehai Wang, Yu Lu, Haoji Hu
Date: 2018-04-25 00:00:00
Tasks: Network Pruning

Parameter pruning is a promising approach for CNN compression and
acceleration by eliminating redundant model parameters with tolerable
performance degrade. Despite its effectiveness, existing regularization-based
parameter pruning methods usually drive weights towards zero with large and
constant regularization factors, which neglects the fragility of the
expressiveness of CNNs, and thus calls for a more gentle regularization scheme
so that the networks can adapt during pruning. To achieve this, we propose a
new and novel regularization-based pruning method, named IncReg, to
incrementally assign different regularization factors to different weights
based on their relative importance. Empirical analysis on CIFAR-10 dataset
verifies the merits of IncReg. Further extensive experiments with popular CNNs
on CIFAR-10 and ImageNet datasets show that IncReg achieves comparable to even
better results compared with state-of-the-arts. Our source codes and trained
models are available here: https://github.com/mingsun-tse/caffe_increg.
Analytical Modeling of Vanishing Points and Curves in Catadioptric Cameras
Authors: Pedro Miraldo, Francisco Eiras, Srikumar Ramalingam
Date: 2018-04-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Pose Estimation

Vanishing points and vanishing lines are classical geometrical concepts in
perspective cameras that have a lineage dating back to 3 centuries. A vanishing
point is a point on the image plane where parallel lines in 3D space appear to
converge, whereas a vanishing line passes through 2 or more vanishing points.
While such concepts are simple and intuitive in perspective cameras, their
counterparts in catadioptric cameras (obtained using mirrors and lenses) are
more involved. For example, lines in the 3D space map to higher degree curves
in catadioptric cameras. The projection of a set of 3D parallel lines converges
on a single point in perspective images, whereas they converge to more than one
point in catadioptric cameras. To the best of our knowledge, we are not aware
of any systematic development of analytical models for vanishing points and
vanishing curves in different types of catadioptric cameras. In this paper, we
derive parametric equations for vanishing points and vanishing curves using the
calibration parameters, mirror shape coefficients, and direction vectors of
parallel lines in 3D space. We show compelling experimental results on
vanishing point estimation and absolute pose estimation for a wide range of
catadioptric cameras in both simulations and real experiments.
Relational Marginal Problems: Theory and Estimation
Authors: Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert
Date: 2017-09-18 00:00:00

In the propositional setting, the marginal problem is to find a
(maximum-entropy) distribution that has some given marginals. We study this
problem in a relational setting and make the following contributions. First, we
compare two different notions of relational marginals. Second, we show a
duality between the resulting relational marginal problems and the maximum
likelihood estimation of the parameters of relational models, which generalizes
a well-known duality from the propositional setting. Third, by exploiting the
relational marginal formulation, we present a statistically sound method to
learn the parameters of relational models that will be applied in settings
where the number of constants differs between the training and test data.
Furthermore, based on a relational generalization of marginal polytopes, we
characterize cases where the standard estimators based on feature's number of
true groundings needs to be adjusted and we quantitatively characterize the
consequences of these adjustments. Fourth, we prove bounds on expected errors
of the estimated parameters, which allows us to lower-bound, among other
things, the effective sample size of relational training data.
Dynamic Few-Shot Visual Learning without Forgetting
Authors: Spyros Gidaris, Nikos Komodakis
Date: 2018-04-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Few-Shot Image Classification, Few-Shot Learning, General Classification, Novel Concepts, Object Recognition, One-Shot Learning

The human visual system has the remarkably ability to be able to effortlessly
learn novel concepts from only a few examples. Mimicking the same behavior on
machine learning vision systems is an interesting and very challenging research
problem with many practical advantages on real world vision applications. In
this context, the goal of our work is to devise a few-shot visual learning
system that during test time it will be able to efficiently learn novel
categories from only a few training data while at the same time it will not
forget the initial categories on which it was trained (here called base
categories). To achieve that goal we propose (a) to extend an object
recognition system with an attention based few-shot classification weight
generator, and (b) to redesign the classifier of a ConvNet model as the cosine
similarity function between feature representations and classification weight
vectors. The latter, apart from unifying the recognition of both novel and base
categories, it also leads to feature representations that generalize better on
"unseen" categories. We extensively evaluate our approach on Mini-ImageNet
where we manage to improve the prior state-of-the-art on few-shot recognition
(i.e., we achieve 56.20% and 73.00% on the 1-shot and 5-shot settings
respectively) while at the same time we do not sacrifice any accuracy on the
base categories, which is a characteristic that most prior approaches lack.
Finally, we apply our approach on the recently introduced few-shot benchmark of
Bharath and Girshick [4] where we also achieve state-of-the-art results. The
code and models of our paper will be published on:
https://github.com/gidariss/FewShotWithoutForgetting
Notes on stable learning with piecewise-linear basis functions
Authors: Winfried Lohmiller, Philipp Gassert, Jean-Jacques Slotine
Date: 2018-04-25 00:00:00

We discuss technical results on learning function approximations using
piecewise-linear basis functions, and analyze their stability and convergence
using nonlinear contraction theory.
TOMAAT: volumetric medical image analysis as a cloud service
Authors: Fausto Milletari, Johann Frei, Seyed-Ahmad Ahmadi
Date: 2018-03-19 00:00:00
Tasks: Deep Learning, Medical Image Analysis

Deep learning has been recently applied to a multitude of computer vision and
medical image analysis problems. Although recent research efforts have improved
the state of the art, most of the methods cannot be easily accessed, compared
or used by either researchers or the general public. Researchers often publish
their code and trained models on the internet, but this does not always enable
these approaches to be easily used or integrated in stand-alone applications
and existing workflows. In this paper we propose a framework which allows easy
deployment and access of deep learning methods for segmentation through a
cloud-based architecture. Our approach comprises three parts: a server, which
wraps trained deep learning models and their pre- and post-processing data
pipelines and makes them available on the cloud; a client which interfaces with
the server to obtain predictions on user data; a service registry that informs
clients about available prediction endpoints that are available in the cloud.
These three parts constitute the open-source TOMAAT framework.
Weakly-Supervised Learning-Based Feature Localization in Confocal Laser Endomicroscopy Glioma Images
Authors: Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Claudio Cavallo, Xiaochun Zhao, Sirin Gandhi, Leandro Borba Moreira, Jennifer Eschbacher, Peter Nakaji, Mark C. Preul, Yezhou Yang
Date: 2018-04-25 00:00:00
Tasks: Decision Making, Diagnostic, Image Segmentation, object-detection, Object Detection, Semantic Segmentation, Weakly-supervised Learning

Confocal Laser Endomicroscope (CLE) is a novel handheld fluorescence imaging
device that has shown promise for rapid intraoperative diagnosis of brain tumor
tissue. Currently CLE is capable of image display only and lacks an automatic
system to aid the surgeon in analyzing the images. The goal of this project was
to develop a computer-aided diagnostic approach for CLE imaging of human glioma
with feature localization function. Despite the tremendous progress in object
detection and image segmentation methods in recent years, most of such methods
require large annotated datasets for training. However, manual annotation of
thousands of histopathological images by physicians is costly and time
consuming. To overcome this problem, we propose a Weakly-Supervised Learning
(WSL)-based model for feature localization that trains on image-level
annotations, and then localizes incidences of a class-of-interest in the test
image. We developed a novel convolutional neural network for diagnostic
features localization from CLE images by employing a novel multiscale
activation map that is laterally inhibited and collaterally integrated. To
validate our method, we compared proposed model's output to the manual
annotation performed by four neurosurgeons on test images. Proposed model
achieved 88% mean accuracy and 86% mean intersection over union on intermediate
features and 87% mean accuracy and 88% mean intersection over union on
restrictive fine features, while outperforming other state of the art methods
tested. This system can improve accuracy and efficiency in characterization of
CLE images of glioma tissue during surgery, augment intraoperative
decision-making process regarding tumor margin and affect resection rates.
A Note on Kernel Methods for Multiscale Systems with Critical Transitions
Authors: Boumediene Hamzi, Christian Kuehn, Sameh Mohamed
Date: 2018-04-25 00:00:00

We study the maximum mean discrepancy (MMD) in the context of critical
transitions modelled by fast-slow stochastic dynamical systems. We establish a
new link between the dynamical theory of critical transitions with the
statistical aspects of the MMD. In particular, we show that a formal
approximation of the MMD near fast subsystem bifurcation points can be computed
to leading-order. In particular, this leading order approximation shows that
the MMD depends intricately on the fast-slow systems parameters and one can
only expect to extract warning signs under rather stringent conditions.
However, the MMD turns out to be an excellent binary classifier to detect the
change point induced by the critical transition. We cross-validate our results
by numerical simulations for a van der Pol-type model.