pwc-archive/papers-with-abstracts huggingface dataset:
Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising
Authors: Peng Liu, Ruogu Fang
Date: 2017-07-28 00:00:00
Tasks: Denoising, Image Denoising

In this work, we explore an innovative strategy for image denoising by using
convolutional neural networks (CNN) to learn pixel-distribution from noisy
data. By increasing CNN's width with large reception fields and more channels
in each layer, CNNs can reveal the ability to learn pixel-distribution, which
is a prior existing in many different types of noise. The key to our approach
is a discovery that wider CNNs tends to learn the pixel-distribution features,
which provides the probability of that inference-mapping primarily relies on
the priors instead of deeper CNNs with more stacked nonlinear layers. We
evaluate our work: Wide inference Networks (WIN) on additive white Gaussian
noise (AWGN) and demonstrate that by learning the pixel-distribution in images,
WIN-based network consistently achieves significantly better performance than
current state-of-the-art deep CNN-based methods in both quantitative and visual
evaluations. \textit{Code and models are available at
\url{https://github.com/cswin/WIN}}.
Relative Camera Pose Estimation Using Convolutional Neural Networks
Authors: Iaroslav Melekhov, Juha Ylioinas, Juho Kannala, Esa Rahtu
Date: 2017-02-05 00:00:00
Tasks: Camera Pose Estimation, General Classification, Pose Estimation, Transfer Learning, Translation

This paper presents a convolutional neural network based approach for
estimating the relative pose between two cameras. The proposed network takes
RGB images from both cameras as input and directly produces the relative
rotation and translation as output. The system is trained in an end-to-end
manner utilising transfer learning from a large scale classification dataset.
The introduced approach is compared with widely used local feature based
methods (SURF, ORB) and the results indicate a clear improvement over the
baseline. In addition, a variant of the proposed architecture containing a
spatial pyramid pooling (SPP) layer is evaluated and shown to further improve
the performance.
Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning
Authors: Ziliang Chen, Keze Wang, Xiao Wang, Pai Peng, Ebroul Izquierdo, Liang Lin
Date: 2017-07-28 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Aiming at improving performance of visual classification in a cost-effective
manner, this paper proposes an incremental semi-supervised learning paradigm
called Deep Co-Space (DCS). Unlike many conventional semi-supervised learning
methods usually performing within a fixed feature space, our DCS gradually
propagates information from labeled samples to unlabeled ones along with deep
feature learning. We regard deep feature learning as a series of steps pursuing
feature transformation, i.e., projecting the samples from a previous space into
a new one, which tends to select the reliable unlabeled samples with respect to
this setting. Specifically, for each unlabeled image instance, we measure its
reliability by calculating the category variations of feature transformation
from two different neighborhood variation perspectives, and merged them into an
unified sample mining criterion deriving from Hellinger distance. Then, those
samples keeping stable correlation to their neighboring samples (i.e., having
small category variation in distribution) across the successive feature space
transformation, are automatically received labels and incorporated into the
model for incrementally training in terms of classification. Our extensive
experiments on standard image classification benchmarks (e.g., Caltech-256 and
SUN-397) demonstrate that the proposed framework is capable of effectively
mining from large-scale unlabeled images, which boosts image classification
performance and achieves promising results compared to other semi-supervised
learning methods.
Automatic Classification of Bright Retinal Lesions via Deep Network Features
Authors: Ibrahim Sadek, Mohamed Elawady, Abd El Rahman Shabayek
Date: 2017-07-07 00:00:00
Tasks: General Classification

The diabetic retinopathy is timely diagonalized through color eye fundus
images by experienced ophthalmologists, in order to recognize potential retinal
features and identify early-blindness cases. In this paper, it is proposed to
extract deep features from the last fully-connected layer of, four different,
pre-trained convolutional neural networks. These features are then feeded into
a non-linear classifier to discriminate three-class diabetic cases, i.e.,
normal, exudates, and drusen. Averaged across 1113 color retinal images
collected from six publicly available annotated datasets, the deep features
approach perform better than the classical bag-of-words approach. The proposed
approaches have an average accuracy between 91.23% and 92.00% with more than
13% improvement over the traditional state of art methods.
Adaptive Inferential Method for Monotone Graph Invariants
Authors: Junwei Lu, Matey Neykov, Han Liu
Date: 2017-07-28 00:00:00
Tasks: valid

We consider the problem of undirected graphical model inference. In many
applications, instead of perfectly recovering the unknown graph structure, a
more realistic goal is to infer some graph invariants (e.g., the maximum
degree, the number of connected subgraphs, the number of isolated nodes). In
this paper, we propose a new inferential framework for testing nested multiple
hypotheses and constructing confidence intervals of the unknown graph
invariants under undirected graphical models. Compared to perfect graph
recovery, our methods require significantly weaker conditions. This paper makes
two major contributions: (i) Methodologically, for testing nested multiple
hypotheses, we propose a skip-down algorithm on the whole family of monotone
graph invariants (The invariants which are non-decreasing under addition of
edges). We further show that the same skip-down algorithm also provides valid
confidence intervals for the targeted graph invariants. (ii) Theoretically, we
prove that the length of the obtained confidence intervals are optimal and
adaptive to the unknown signal strength. We also prove generic lower bounds for
the confidence interval length for various invariants. Numerical results on
both synthetic simulations and a brain imaging dataset are provided to
illustrate the usefulness of the proposed method.
Improved Face Detection and Alignment using Cascade Deep Convolutional Network
Authors: Weilin Cong, Sanyuan Zhao, Hui Tian, Jianbing Shen
Date: 2017-07-28 00:00:00
Tasks: Face Detection

Real-world face detection and alignment demand an advanced discriminative
model to address challenges by pose, lighting and expression. Illuminated by
the deep learning algorithm, some convolutional neural networks based face
detection and alignment methods have been proposed. Recent studies have
utilized the relation between face detection and alignment to make models
computationally efficiency, however they ignore the connection between each
cascade CNNs. In this paper, we propose an structure to propose higher quality
training data for End-to-End cascade network training, which give computers
more space to automatic adjust weight parameter and accelerate convergence.
Experiments demonstrate considerable improvement over existing detection and
alignment models.
Ultraslow diffusion in language: Dynamics of appearance of already popular adjectives on Japanese blogs
Authors: Hayafumi Watanabe
Date: 2017-07-21 00:00:00
Tasks: Articles, Time Series, Time Series Analysis

What dynamics govern a time series representing the appearance of words in
social media data? In this paper, we investigate an elementary dynamics, from
which word-dependent special effects are segregated, such as breaking news,
increasing (or decreasing) concerns, or seasonality. To elucidate this problem,
we investigated approximately three billion Japanese blog articles over a
period of six years, and analysed some corresponding solvable mathematical
models. From the analysis, we found that a word appearance can be explained by
the random diffusion model based on the power-law forgetting process, which is
a type of long memory point process related to ARFIMA(0,0.5,0). In particular,
we confirmed that ultraslow diffusion (where the mean squared displacement
grows logarithmically), which the model predicts in an approximate manner,
reproduces the actual data. In addition, we also show that the model can
reproduce other statistical properties of a time series: (i) the fluctuation
scaling, (ii) spectrum density, and (iii) shapes of the probability density
functions.
Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization
Authors: Frederick Tung, Srikanth Muralidharan, Greg Mori
Date: 2017-07-28 00:00:00
Tasks: Bayesian Optimization, Network Pruning

When approaching a novel visual recognition problem in a specialized image
domain, a common strategy is to start with a pre-trained deep neural network
and fine-tune it to the specialized domain. If the target domain covers a
smaller visual space than the source domain used for pre-training (e.g.
ImageNet), the fine-tuned network is likely to be over-parameterized. However,
applying network pruning as a post-processing step to reduce the memory
requirements has drawbacks: fine-tuning and pruning are performed
independently; pruning parameters are set once and cannot adapt over time; and
the highly parameterized nature of state-of-the-art pruning methods make it
prohibitive to manually search the pruning parameter space for deep networks,
leading to coarse approximations. We propose a principled method for jointly
fine-tuning and compressing a pre-trained convolutional network that overcomes
these limitations. Experiments on two specialized image domains (remote sensing
images and describable textures) demonstrate the validity of the proposed
approach.
Exploiting Web Images for Weakly Supervised Object Detection
Authors: Qingyi Tao, Hao Yang, Jianfei Cai
Date: 2017-07-27 00:00:00
Tasks: Diversity, Object, object-detection, Object Detection, Transfer Learning, Weakly Supervised Object Detection

In recent years, the performance of object detection has advanced
significantly with the evolving deep convolutional neural networks. However,
the state-of-the-art object detection methods still rely on accurate bounding
box annotations that require extensive human labelling. Object detection
without bounding box annotations, i.e, weakly supervised detection methods, are
still lagging far behind. As weakly supervised detection only uses image level
labels and does not require the ground truth of bounding box location and label
of each object in an image, it is generally very difficult to distill knowledge
of the actual appearances of objects. Inspired by curriculum learning, this
paper proposes an easy-to-hard knowledge transfer scheme that incorporates easy
web images to provide prior knowledge of object appearance as a good starting
point. While exploiting large-scale free web imagery, we introduce a
sophisticated labour free method to construct a web dataset with good diversity
in object appearance. After that, semantic relevance and distribution relevance
are introduced and utilized in the proposed curriculum training scheme. Our
end-to-end learning with the constructed web data achieves remarkable
improvement across most object classes especially for the classes that are
often considered hard in other works.
Learning Infinite-Layer Networks: Without the Kernel Trick
Authors: Roi Livni, Daniel Carmon, Amir Globerson
Date: 2016-06-16 00:00:00

Infinite--Layer Networks (ILN) have recently been proposed as an architecture
that mimics neural networks while enjoying some of the advantages of kernel
methods. ILN are networks that integrate over infinitely many nodes within a
single hidden layer. It has been demonstrated by several authors that the
problem of learning ILN can be reduced to the kernel trick, implying that
whenever a certain integral can be computed analytically they are efficiently
learnable.
  In this work we give an online algorithm for ILN, which avoids the kernel
trick assumption. More generally and of independent interest, we show that
kernel methods in general can be exploited even when the kernel cannot be
efficiently computed but can only be estimated via sampling.
  We provide a regret analysis for our algorithm, showing that it matches the
sample complexity of methods which have access to kernel values. Thus, our
method is the first to demonstrate that the kernel trick is not necessary as
such, and random features suffice to obtain comparable performance.
Object Detection of Satellite Images Using Multi-Channel Higher-order Local Autocorrelation
Authors: Kazuki Uehara, Hidenori Sakanashi, Hirokazu Nosato, Masahiro Murakawa, Hiroki Miyamoto, Ryosuke Nakamura
Date: 2017-07-28 00:00:00
Tasks: Earth Observation, Object, object-detection, Object Detection

The Earth observation satellites have been monitoring the earth's surface for
a long time, and the images taken by the satellites contain large amounts of
valuable data. However, it is extremely hard work to manually analyze such huge
data. Thus, a method of automatic object detection is needed for satellite
images to facilitate efficient data analyses. This paper describes a new image
feature extended from higher-order local autocorrelation to the object
detection of multispectral satellite images. The feature has been extended to
extract spectral inter-relationships in addition to spatial relationships to
fully exploit multispectral information. The results of experiments with object
detection tasks conducted to evaluate the effectiveness of the proposed feature
extension indicate that the feature realized a higher performance compared to
existing methods.
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension
Authors: Boyuan Pan, Hao Li, Zhou Zhao, Bin Cao, Deng Cai, Xiaofei He
Date: 2017-07-28 00:00:00
Tasks: Question Answering, Reading Comprehension, Sentence, TriviaQA

Machine comprehension(MC) style question answering is a representative
problem in natural language processing. Previous methods rarely spend time on
the improvement of encoding layer, especially the embedding of syntactic
information and name entity of the words, which are very crucial to the quality
of encoding. Moreover, existing attention methods represent each query word as
a vector or use a single vector to represent the whole query sentence, neither
of them can handle the proper weight of the key words in query sentence. In
this paper, we introduce a novel neural network architecture called Multi-layer
Embedding with Memory Network(MEMEN) for machine reading task. In the encoding
layer, we employ classic skip-gram model to the syntactic and semantic
information of the words to train a new kind of embedding layer. We also
propose a memory network of full-orientation matching of the query and passage
to catch more pivotal information. Experiments show that our model has
competitive results both from the perspectives of precision and efficiency in
Stanford Question Answering Dataset(SQuAD) among all published results and
achieves the state-of-the-art results on TriviaQA dataset.
An Open Source C++ Implementation of Multi-Threaded Gaussian Mixture Models, k-Means and Expectation Maximisation
Authors: Conrad Sanderson, Ryan Curtin
Date: 2017-07-28 00:00:00

Modelling of multivariate densities is a core component in many signal
processing, pattern recognition and machine learning applications. The
modelling is often done via Gaussian mixture models (GMMs), which use
computationally expensive and potentially unstable training algorithms. We
provide an overview of a fast and robust implementation of GMMs in the C++
language, employing multi-threaded versions of the Expectation Maximisation
(EM) and k-means training algorithms. Multi-threading is achieved through
reformulation of the EM and k-means algorithms into a MapReduce-like framework.
Furthermore, the implementation uses several techniques to improve numerical
stability and modelling accuracy. We demonstrate that the multi-threaded
implementation achieves a speedup of an order of magnitude on a recent 16 core
machine, and that it can achieve higher modelling accuracy than a previously
well-established publically accessible implementation. The multi-threaded
implementation is included as a user-friendly class in recent releases of the
open source Armadillo C++ linear algebra library. The library is provided under
the permissive Apache~2.0 license, allowing unencumbered use in commercial
products.
Reducing Drift in Visual Odometry by Inferring Sun Direction Using a Bayesian Convolutional Neural Network
Authors: Valentin Peretroukhin, Lee Clement, Jonathan Kelly
Date: 2016-09-20 00:00:00
Tasks: Visual Odometry

We present a method to incorporate global orientation information from the sun into a visual odometry pipeline using only the existing image stream, where the sun is typically not visible. We leverage recent advances in Bayesian Convolutional Neural Networks to train and implement a sun detection model that infers a three-dimensional sun direction vector from a single RGB image. Crucially, our method also computes a principled uncertainty associated with each prediction, using a Monte Carlo dropout scheme. We incorporate this uncertainty into a sliding window stereo visual odometry pipeline where accurate uncertainty estimates are critical for optimal data fusion. Our Bayesian sun detection model achieves a median error of approximately 12 degrees on the KITTI odometry benchmark training set, and yields improvements of up to 42% in translational ARMSE and 32% in rotational ARMSE compared to standard VO. An open source implementation of our Bayesian CNN sun estimator (Sun-BCNN) using Caffe is available at https://github. com/utiasSTARS/sun-bcnn-vo
Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
Authors: Ke Gong, Xiaodan Liang, Dongyu Zhang, Xiaohui Shen, Liang Lin
Date: 2017-03-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Human Parsing, Self-Supervised Learning, Semantic Segmentation

Human parsing has recently attracted a lot of research interests due to its
huge application potentials. However existing datasets have limited number of
images and annotations, and lack the variety of human appearances and the
coverage of challenging cases in unconstrained environment. In this paper, we
introduce a new benchmark "Look into Person (LIP)" that makes a significant
advance in terms of scalability, diversity and difficulty, a contribution that
we feel is crucial for future developments in human-centric analysis. This
comprehensive dataset contains over 50,000 elaborately annotated images with 19
semantic part labels, which are captured from a wider range of viewpoints,
occlusions and background complexity. Given these rich annotations we perform
detailed analyses of the leading human parsing approaches, gaining insights
into the success and failures of these methods. Furthermore, in contrast to the
existing efforts on improving the feature discriminative capability, we solve
human parsing by exploring a novel self-supervised structure-sensitive learning
approach, which imposes human pose structures into parsing results without
resorting to extra supervision (i.e., no need for specifically labeling human
joints in model training). Our self-supervised learning framework can be
injected into any advanced neural networks to help incorporate rich high-level
knowledge regarding human joints from a global perspective and improve the
parsing results. Extensive evaluations on our LIP and the public
PASCAL-Person-Part dataset demonstrate the superiority of our method.
Learning to Teach Reinforcement Learning Agents
Authors: Anestis Fachantidis, Matthew E. Taylor, Ioannis Vlahavas
Date: 2017-07-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

In this article we study the transfer learning model of action advice under a
budget. We focus on reinforcement learning teachers providing action advice to
heterogeneous students playing the game of Pac-Man under a limited advice
budget. First, we examine several critical factors affecting advice quality in
this setting, such as the average performance of the teacher, its variance and
the importance of reward discounting in advising. The experiments show the
non-trivial importance of the coefficient of variation (CV) as a statistic for
choosing policies that generate advice. The CV statistic relates variance to
the corresponding mean. Second, the article studies policy learning for
distributing advice under a budget. Whereas most methods in the relevant
literature rely on heuristics for advice distribution we formulate the problem
as a learning one and propose a novel RL algorithm capable of learning when to
advise, adapting to the student and the task at hand. Furthermore, we argue
that learning to advise under a budget is an instance of a more generic
learning problem: Constrained Exploitation Reinforcement Learning.
Domain Recursion for Lifted Inference with Existential Quantifiers
Authors: Seyed Mehran Kazemi, Angelika Kimmig, Guy Van Den Broeck, David Poole
Date: 2017-07-24 00:00:00

In recent work, we proved that the domain recursion inference rule makes
domain-lifted inference possible on several relational probability models
(RPMs) for which the best known time complexity used to be exponential. We also
identified two classes of RPMs for which inference becomes domain lifted when
using domain recursion. These two classes subsume the largest lifted classes
that were previously known. In this paper, we show that domain recursion can
also be applied to models with existential quantifiers. Currently, all lifted
inference algorithms assume that existential quantifiers have been removed in
pre-processing by Skolemization. We show that besides introducing potentially
inconvenient negative weights, Skolemization may increase the time complexity
of inference. We give two example models where domain recursion can replace
Skolemization, avoids the need for dealing with negative numbers, and reduces
the time complexity of inference. These two examples may be interesting from
three theoretical aspects: 1- they provide a better and deeper understanding of
domain recursion and, in general, (lifted) inference, 2- they may serve as
evidence that there are larger classes of models for which domain recursion can
satisfyingly replace Skolemization, and 3- they may serve as evidence that
better Skolemization techniques exist.
Learning from Video and Text via Large-Scale Discriminative Clustering
Authors: Antoine Miech, Jean-Baptiste Alayrac, Piotr Bojanowski, Ivan Laptev, Josef Sivic
Date: 2017-07-27 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Recognition, Clustering, Temporal Action Localization, Video Alignment, Video Retrieval, Weakly-Supervised Action Recognition, Weakly-supervised Learning

Discriminative clustering has been successfully applied to a number of
weakly-supervised learning tasks. Such applications include person and action
recognition, text-to-video alignment, object co-segmentation and colocalization
in videos and images. One drawback of discriminative clustering, however, is
its limited scalability. We address this issue and propose an online
optimization algorithm based on the Block-Coordinate Frank-Wolfe algorithm. We
apply the proposed method to the problem of weakly supervised learning of
actions and actors from movies together with corresponding movie scripts. The
scaling up of the learning problem to 66 feature length movies enables us to
significantly improve weakly supervised action recognition.