pwc-archive/papers-with-abstracts huggingface dataset:
Contour Detection from Deep Patch-level Boundary Prediction
Authors: Teck Wee Chua, Li Shen
Date: 2017-05-09 00:00:00
Tasks: Contour Detection, Prediction

In this paper, we present a novel approach for contour detection with
Convolutional Neural Networks. A multi-scale CNN learning framework is designed
to automatically learn the most relevant features for contour patch detection.
Our method uses patch-level measurements to create contour maps with
overlapping patches. We show the proposed CNN is able to to detect large-scale
contours in an image efficienly. We further propose a guided filtering method
to refine the contour maps produced from large-scale contours. Experimental
results on the major contour benchmark databases demonstrate the effectiveness
of the proposed technique. We show our method can achieve good detection of
both fine-scale and large-scale contours.
Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition
Authors: Xi Yin, Xiaoming Liu
Date: 2017-02-15 00:00:00
Tasks: Face Recognition, Multi-Task Learning, Robust Face Recognition

This paper explores multi-task learning (MTL) for face recognition. We answer
the questions of how and why MTL can improve the face recognition performance.
First, we propose a multi-task Convolutional Neural Network (CNN) for face
recognition where identity classification is the main task and pose,
illumination, and expression estimations are the side tasks. Second, we develop
a dynamic-weighting scheme to automatically assign the loss weight to each side
task, which is a crucial problem in MTL. Third, we propose a pose-directed
multi-task CNN by grouping different poses to learn pose-specific identity
features, simultaneously across all poses. Last but not least, we propose an
energy-based weight analysis method to explore how CNN-based MTL works. We
observe that the side tasks serve as regularizations to disentangle the
variations from the learnt identity features. Extensive experiments on the
entire Multi-PIE dataset demonstrate the effectiveness of the proposed
approach. To the best of our knowledge, this is the first work using all data
in Multi-PIE for face recognition. Our approach is also applicable to
in-the-wild datasets for pose-invariant face recognition and achieves
comparable or better performance than state of the art on LFW, CFP, and IJB-A
datasets.
Region Ensemble Network: Improving Convolutional Network for Hand Pose Estimation
Authors: Hengkai Guo, Guijin Wang, Xinghao Chen, Cairong Zhang, Fei Qiao, Huazhong Yang
Date: 2017-02-08 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation, regression

Hand pose estimation from monocular depth images is an important and
challenging problem for human-computer interaction. Recently deep convolutional
networks (ConvNet) with sophisticated design have been employed to address it,
but the improvement over traditional methods is not so apparent. To promote the
performance of directly 3D coordinate regression, we propose a tree-structured
Region Ensemble Network (REN), which partitions the convolution outputs into
regions and integrates the results from multiple regressors on each regions.
Compared with multi-model ensemble, our model is completely end-to-end
training. The experimental results demonstrate that our approach achieves the
best performance among state-of-the-arts on two public datasets.
Query-adaptive Image Retrieval by Deep Weighted Hashing
Authors: Jian Zhang, Yuxin Peng
Date: 2016-12-08 00:00:00
Tasks: Deep Hashing, Image Retrieval, Retrieval

Hashing methods have attracted much attention for large scale image
retrieval. Some deep hashing methods have achieved promising results by taking
advantage of the strong representation power of deep networks recently.
However, existing deep hashing methods treat all hash bits equally. On one
hand, a large number of images share the same distance to a query image due to
the discrete Hamming distance, which raises a critical issue of image retrieval
where fine-grained rankings are very important. On the other hand, different
hash bits actually contribute to the image retrieval differently, and treating
them equally greatly affects the retrieval accuracy of image. To address the
above two problems, we propose the query-adaptive deep weighted hashing (QaDWH)
approach, which can perform fine-grained ranking for different queries by
weighted Hamming distance. First, a novel deep hashing network is proposed to
learn the hash codes and corresponding class-wise weights jointly, so that the
learned weights can reflect the importance of different hash bits for different
image classes. Second, a query-adaptive image retrieval method is proposed,
which rapidly generates hash bit weights for different query images by fusing
its semantic probability and the learned class-wise weights. Fine-grained image
retrieval is then performed by the weighted Hamming distance, which can provide
more accurate ranking than the traditional Hamming distance. Experiments on
four widely used datasets show that the proposed approach outperforms eight
state-of-the-art hashing methods.
Deep Spatio-temporal Manifold Network for Action Recognition
Authors: Ce Li, Chen Chen, Baochang Zhang, Qixiang Ye, Jungong Han, Rongrong Ji
Date: 2017-05-09 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Visual data such as videos are often sampled from complex manifold. We
propose leveraging the manifold structure to constrain the deep action feature
learning, thereby minimizing the intra-class variations in the feature space
and alleviating the over-fitting problem. Considering that manifold can be
transferred, layer by layer, from the data domain to the deep features, the
manifold priori is posed from the top layer into the back propagation learning
procedure of convolutional neural network (CNN). The resulting algorithm
--Spatio-Temporal Manifold Network-- is solved with the efficient Alternating
Direction Method of Multipliers and Backward Propagation (ADMM-BP). We
theoretically show that STMN recasts the problem as projection over the
manifold via an embedding method. The proposed approach is evaluated on two
benchmark datasets, showing significant improvements to the baselines.
Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures
Authors: Daniel R. Jiang, Warren B. Powell
Date: 2015-09-07 00:00:00

In this paper, we consider a finite-horizon Markov decision process (MDP) for
which the objective at each stage is to minimize a quantile-based risk measure
(QBRM) of the sequence of future costs; we call the overall objective a dynamic
quantile-based risk measure (DQBRM). In particular, we consider optimizing
dynamic risk measures where the one-step risk measures are QBRMs, a class of
risk measures that includes the popular value at risk (VaR) and the conditional
value at risk (CVaR). Although there is considerable theoretical development of
risk-averse MDPs in the literature, the computational challenges have not been
explored as thoroughly. We propose data-driven and simulation-based approximate
dynamic programming (ADP) algorithms to solve the risk-averse sequential
decision problem. We address the issue of inefficient sampling for risk
applications in simulated settings and present a procedure, based on importance
sampling, to direct samples toward the "risky region" as the ADP algorithm
progresses. Finally, we show numerical results of our algorithms in the context
of an application involving risk-averse bidding for energy storage.
Theoretical and Practical Advances on Smoothing for Extensive-Form Games
Authors: Christian Kroer, Kevin Waugh, Fatma Kilinc-Karzan, Tuomas Sandholm
Date: 2017-02-16 00:00:00
Tasks: counterfactual, Form

Sparse iterative methods, in particular first-order methods, are known to be
among the most effective in solving large-scale two-player zero-sum
extensive-form games. The convergence rates of these methods depend heavily on
the properties of the distance-generating function that they are based on. We
investigate the acceleration of first-order methods for solving extensive-form
games through better design of the dilated entropy function---a class of
distance-generating functions related to the domains associated with the
extensive-form games. By introducing a new weighting scheme for the dilated
entropy function, we develop the first distance-generating function for the
strategy spaces of sequential games that has no dependence on the branching
factor of the player. This result improves the convergence rate of several
first-order methods by a factor of $\Omega(b^dd)$, where $b$ is the branching
factor of the player, and $d$ is the depth of the game tree.
  Thus far, counterfactual regret minimization methods have been faster in
practice, and more popular, than first-order methods despite their
theoretically inferior convergence rates. Using our new weighting scheme and
practical tuning we show that, for the first time, the excessive gap technique
can be made faster than the fastest counterfactual regret minimization
algorithm, CFR+, in practice.
A Survey of Structure from Motion
Authors: Onur Ozyesil, Vladislav Voroninski, Ronen Basri, Amit Singer
Date: 2017-01-30 00:00:00
Tasks: Motion Estimation, Simultaneous Localization and Mapping, Survey

The structure from motion (SfM) problem in computer vision is the problem of
recovering the three-dimensional ($3$D) structure of a stationary scene from a
set of projective measurements, represented as a collection of two-dimensional
($2$D) images, via estimation of motion of the cameras corresponding to these
images. In essence, SfM involves the three main stages of (1) extraction of
features in images (e.g., points of interest, lines, etc.) and matching these
features between images, (2) camera motion estimation (e.g., using relative
pairwise camera positions estimated from the extracted features), and (3)
recovery of the $3$D structure using the estimated motion and features (e.g.,
by minimizing the so-called reprojection error). This survey mainly focuses on
relatively recent developments in the literature pertaining to stages (2) and
(3). More specifically, after touching upon the early factorization-based
techniques for motion and structure estimation, we provide a detailed account
of some of the recent camera location estimation methods in the literature,
followed by discussion of notable techniques for $3$D structure recovery. We
also cover the basics of the simultaneous localization and mapping (SLAM)
problem, which can be viewed as a specific case of the SfM problem. Further,
our survey includes a review of the fundamentals of feature extraction and
matching (i.e., stage (1) above), various recent methods for handling
ambiguities in $3$D scenes, SfM techniques involving relatively uncommon camera
models and image features, and popular sources of data and SfM software.
Geometric GAN
Authors: Jae Hyun Lim, Jong Chul Ye
Date: 2017-05-08 00:00:00
Tasks: Text Generation

Generative Adversarial Nets (GANs) represent an important milestone for
effective generative models, which has inspired numerous variants seemingly
different from each other. One of the main contributions of this paper is to
reveal a unified geometric structure in GAN and its variants. Specifically, we
show that the adversarial generative model training can be decomposed into
three geometric steps: separating hyperplane search, discriminator parameter
update away from the separating hyperplane, and the generator update along the
normal vector direction of the separating hyperplane. This geometric intuition
reveals the limitations of the existing approaches and leads us to propose a
new formulation called geometric GAN using SVM separating hyperplane that
maximizes the margin. Our theoretical analysis shows that the geometric GAN
converges to a Nash equilibrium between the discriminator and generator. In
addition, extensive numerical results show that the superior performance of
geometric GAN.
Stabilized Sparse Online Learning for Sparse Data
Authors: Yuting Ma, Tian Zheng
Date: 2016-04-21 00:00:00

Stochastic gradient descent (SGD) is commonly used for optimization in
large-scale machine learning problems. Langford et al. (2009) introduce a
sparse online learning method to induce sparsity via truncated gradient. With
high-dimensional sparse data, however, the method suffers from slow convergence
and high variance due to the heterogeneity in feature sparsity. To mitigate
this issue, we introduce a stabilized truncated stochastic gradient descent
algorithm. We employ a soft-thresholding scheme on the weight vector where the
imposed shrinkage is adaptive to the amount of information available in each
feature. The variability in the resulted sparse weight vector is further
controlled by stability selection integrated with the informative truncation.
To facilitate better convergence, we adopt an annealing strategy on the
truncation rate, which leads to a balanced trade-off between exploration and
exploitation in learning a sparse weight vector. Numerical experiments show
that our algorithm compares favorably with the original algorithm in terms of
prediction accuracy, achieved sparsity and stability.
RENOIR - A Dataset for Real Low-Light Image Noise Reduction
Authors: Josue Anaya, Adrian Barbu
Date: 2014-09-29 00:00:00
Tasks: Color Image Denoising, Denoising, Image Denoising, Noise Estimation

Image denoising algorithms are evaluated using images corrupted by artificial
noise, which may lead to incorrect conclusions about their performances on real
noise. In this paper we introduce a dataset of color images corrupted by
natural noise due to low-light conditions, together with spatially and
intensity-aligned low noise images of the same scenes. We also introduce a
method for estimating the true noise level in our images, since even the low
noise images contain small amounts of noise. We evaluate the accuracy of our
noise estimation method on real and artificial noise, and investigate the
Poisson-Gaussian noise model. Finally, we use our dataset to evaluate six
denoising algorithms: Active Random Field, BM3D, Bilevel-MRF, Multi-Layer
Perceptron, and two versions of NL-means. We show that while the Multi-Layer
Perceptron, Bilevel-MRF, and NL-means with soft threshold outperform BM3D on
gray images with synthetic noise, they lag behind on our dataset.
Here's My Point: Joint Pointer Architecture for Argument Mining
Authors: Peter Potash, Alexey Romanov, Anna Rumshisky
Date: 2016-12-28 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Argument Mining

One of the major goals in automated argumentation mining is to uncover the
argument structure present in argumentative text. In order to determine this
structure, one must understand how different individual components of the
overall argument are linked. General consensus in this field dictates that the
argument components form a hierarchy of persuasion, which manifests itself in a
tree structure. This work provides the first neural network-based approach to
argumentation mining, focusing on the two tasks of extracting links between
argument components, and classifying types of argument components. In order to
solve this problem, we propose to use a joint model that is based on a Pointer
Network architecture. A Pointer Network is appealing for this task for the
following reasons: 1) It takes into account the sequential nature of argument
components; 2) By construction, it enforces certain properties of the tree
structure present in argument relations; 3) The hidden representations can be
applied to auxiliary tasks. In order to extend the contribution of the original
Pointer Network model, we construct a joint model that simultaneously attempts
to learn the type of argument component, as well as continuing to predict links
between argument components. The proposed joint model achieves state-of-the-art
results on two separate evaluation corpora, achieving far superior performance
than a regular Pointer Network model. Our results show that optimizing for both
tasks, and adding a fully-connected layer prior to recurrent neural network
input, is crucial for high performance.
An Anthropic Argument against the Future Existence of Superintelligent Artificial Intelligence
Authors: Toby Pereira
Date: 2017-05-08 00:00:00

This paper uses anthropic reasoning to argue for a reduced likelihood that
superintelligent AI will come into existence in the future. To make this
argument, a new principle is introduced: the Super-Strong Self-Sampling
Assumption (SSSSA), building on the Self-Sampling Assumption (SSA) and the
Strong Self-Sampling Assumption (SSSA). SSA uses as its sample the relevant
observers, whereas SSSA goes further by using observer-moments. SSSSA goes
further still and weights each sample proportionally, according to the size of
a mind in cognitive terms. SSSSA is required for human observer-samples to be
typical, given by how much non-human animals outnumber humans. Given SSSSA, the
assumption that humans experience typical observer-samples relies on a future
where superintelligent AI does not dominate, which in turn reduces the
likelihood of it being created at all.
Geometry of Optimization and Implicit Regularization in Deep Learning
Authors: Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
Date: 2017-05-08 00:00:00
Tasks: Deep Learning

We argue that the optimization plays a crucial role in generalization of deep
learning models through implicit regularization. We do this by demonstrating
that generalization ability is not controlled by network size but rather by
some other implicit control. We then demonstrate how changing the empirical
optimization procedure can improve generalization, even if actual optimization
quality is not affected. We do so by studying the geometry of the parameter
space of deep networks, and devising an optimization algorithm attuned to this
geometry.
Discrete Modeling of Multi-Transmitter Neural Networks with Neuron Competition
Authors: Nikolay Bazenkov, Varvara Dyakonova, Oleg Kuznetsov, Dmitri Sakharov, Dmitry Vorontsov, Liudmila Zhilyakova
Date: 2017-05-05 00:00:00
Tasks: Diversity

We propose a novel discrete model of central pattern generators (CPG),
neuronal ensembles generating rhythmic activity. The model emphasizes the role
of nonsynaptic interactions and the diversity of electrical properties in
nervous systems. Neurons in the model release different neurotransmitters into
the shared extracellular space (ECS) so each neuron with the appropriate set of
receptors can receive signals from other neurons. We consider neurons,
differing in their electrical activity, represented as finite-state machines
functioning in discrete time steps. Discrete modeling is aimed to provide a
computationally tractable and compact explanation of rhythmic pattern
generation in nervous systems. The important feature of the model is the
introduced mechanism of neuronal competition which is shown to be responsible
for the generation of proper rhythms. The model is illustrated with two
examples: a half-center oscillator considered to be a basic mechanism of
emerging rhythmic activity and the well-studied feeding network of a pond
snail. Future research will focus on the neuromodulatory effects ubiquitous in
CPG networks and the whole nervous systems.
Real-Time User-Guided Image Colorization with Learned Deep Priors
Authors: Richard Zhang, Jun-Yan Zhu, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, Alexei A. Efros
Date: 2017-05-08 00:00:00
Tasks: Colorization, Image Colorization, Point-interactive Image Colorization

We propose a deep learning approach for user-guided image colorization. The
system directly maps a grayscale image, along with sparse, local user "hints"
to an output colorization with a Convolutional Neural Network (CNN). Rather
than using hand-defined rules, the network propagates user edits by fusing
low-level cues along with high-level semantic information, learned from
large-scale data. We train on a million images, with simulated user inputs. To
guide the user towards efficient input selection, the system recommends likely
colors based on the input image and current user inputs. The colorization is
performed in a single feed-forward pass, enabling real-time use. Even with
randomly simulated user inputs, we show that the proposed system helps novice
users quickly create realistic colorizations, and offers large improvements in
colorization quality with just a minute of use. In addition, we demonstrate
that the framework can incorporate other user "hints" to the desired
colorization, showing an application to color histogram transfer. Our code and
models are available at https://richzhang.github.io/ideepcolor.
Light Field Video Capture Using a Learning-Based Hybrid Imaging System
Authors: Ting-Chun Wang, Jun-Yan Zhu, Nima Khademi Kalantari, Alexei A. Efros, Ravi Ramamoorthi
Date: 2017-05-08 00:00:00

Light field cameras have many advantages over traditional cameras, as they
allow the user to change various camera settings after capture. However,
capturing light fields requires a huge bandwidth to record the data: a modern
light field camera can only take three images per second. This prevents current
consumer light field cameras from capturing light field videos. Temporal
interpolation at such extreme scale (10x, from 3 fps to 30 fps) is infeasible
as too much information will be entirely missing between adjacent frames.
Instead, we develop a hybrid imaging system, adding another standard video
camera to capture the temporal information. Given a 3 fps light field sequence
and a standard 30 fps 2D video, our system can then generate a full light field
video at 30 fps. We adopt a learning-based approach, which can be decomposed
into two steps: spatio-temporal flow estimation and appearance estimation. The
flow estimation propagates the angular information from the light field
sequence to the 2D video, so we can warp input images to the target view. The
appearance estimation then combines these warped images to output the final
pixels. The whole process is trained end-to-end using convolutional neural
networks. Experimental results demonstrate that our algorithm outperforms
current video interpolation methods, enabling consumer light field videography,
and making applications such as refocusing and parallax view generation
achievable on videos for the first time.
Developing All-Skyrmion Spiking Neural Network
Authors: Zhezhi He, Deliang Fan
Date: 2017-05-08 00:00:00
Tasks: All, Handwritten Digit Recognition

In this work, we have proposed a revolutionary neuromorphic computing
methodology to implement All-Skyrmion Spiking Neural Network (AS-SNN). Such
proposed methodology is based on our finding that skyrmion is a topological
stable spin texture and its spatiotemporal motion along the magnetic nano-track
intuitively interprets the pulse signal transmission between two interconnected
neurons. In such design, spike train in SNN could be encoded as particle-like
skyrmion train and further processed by the proposed skyrmion-synapse and
skyrmion-neuron within the same magnetic nano-track to generate output skyrmion
as post-spike. Then, both pre-neuron spikes and post-neuron spikes are encoded
as particle-like skyrmions without conversion between charge and spin signals,
which fundamentally differentiates our proposed design from other hybrid
Spin-CMOS designs. The system level simulation shows 87.1% inference accuracy
for handwritten digit recognition task, while the energy dissipation is ~1
fJ/per spike which is 3 orders smaller in comparison with CMOS based IBM
TrueNorth system.