pwc-archive/papers-with-abstracts huggingface dataset:
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs
Authors: Xinqing Guo, Zhang Chen, Siyuan Li, Yang Yang, Jingyi Yu
Date: 2017-11-29 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

Human visual system relies on both binocular stereo cues and monocular focusness cues to gain effective 3D perception. In computer vision, the two problems are traditionally solved in separate tracks. In this paper, we present a unified learning-based technique that simultaneously uses both types of cues for depth inference. Specifically, we use a pair of focal stacks as input to emulate human perception. We first construct a comprehensive focal stack training dataset synthesized by depth-guided light field rendering. We then construct three individual networks: a Focus-Net to extract depth from a single focal stack, a EDoF-Net to obtain the extended depth of field (EDoF) image from the focal stack, and a Stereo-Net to conduct stereo matching. We show how to integrate them into a unified BDfF-Net to obtain high-quality depth maps. Comprehensive experiments show that our approach outperforms the state-of-the-art in both accuracy and speed and effectively emulates human vision systems.
Training Neural Networks by Using Power Linear Units (PoLUs)
Authors: Yikang Li, Pak Lun Kevin Ding, Baoxin Li
Date: 2018-02-01 00:00:00
Tasks: image-classification, Image Classification

In this paper, we introduce "Power Linear Unit" (PoLU) which increases the
nonlinearity capacity of a neural network and thus helps improving its
performance. PoLU adopts several advantages of previously proposed activation
functions. First, the output of PoLU for positive inputs is designed to be
identity to avoid the gradient vanishing problem. Second, PoLU has a non-zero
output for negative inputs such that the output mean of the units is close to
zero, hence reducing the bias shift effect. Thirdly, there is a saturation on
the negative part of PoLU, which makes it more noise-robust for negative
inputs. Furthermore, we prove that PoLU is able to map more portions of every
layer's input to the same space by using the power function and thus increases
the number of response regions of the neural network. We use image
classification for comparing our proposed activation function with others. In
the experiments, MNIST, CIFAR-10, CIFAR-100, Street View House Numbers (SVHN)
and ImageNet are used as benchmark datasets. The neural networks we implemented
include widely-used ELU-Network, ResNet-50, and VGG16, plus a couple of shallow
networks. Experimental results show that our proposed activation function
outperforms other state-of-the-art models with most networks.
Dual Recurrent Attention Units for Visual Question Answering
Authors: Ahmed Osman, Wojciech Samek
Date: 2018-02-01 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) requires AI models to comprehend data in two
domains, vision and text. Current state-of-the-art models use learned attention
mechanisms to extract relevant information from the input domains to answer a
certain question. Thus, robust attention mechanisms are essential for powerful
VQA models. In this paper, we propose a recurrent attention mechanism and show
its benefits compared to the traditional convolutional approach. We perform two
ablation studies to evaluate recurrent attention. First, we introduce a
baseline VQA model with visual attention and test the performance difference
between convolutional and recurrent attention on the VQA 2.0 dataset. Secondly,
we design an architecture for VQA which utilizes dual (textual and visual)
Recurrent Attention Units (RAUs). Using this model, we show the effect of all
possible combinations of recurrent and convolutional dual attention. Our single
model outperforms the first place winner on the VQA 2016 challenge and to the
best of our knowledge, it is the second best performing single model on the VQA
1.0 dataset. Furthermore, our model noticeably improves upon the winner of the
VQA 2017 challenge. Moreover, we experiment replacing attention mechanisms in
state-of-the-art models with our RAUs and show increased performance.
Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network
Authors: Zhun Fan, Yuming Wu, Jiewei Lu, Wenji Li
Date: 2018-02-01 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Structured Prediction

Automated pavement crack detection is a challenging task that has been
researched for decades due to the complicated pavement conditions in real
world. In this paper, a supervised method based on deep learning is proposed,
which has the capability of dealing with different pavement conditions.
Specifically, a convolutional neural network (CNN) is used to learn the
structure of the cracks from raw images, without any preprocessing. Small
patches are extracted from crack images as inputs to generate a large training
database, a CNN is trained and crack detection is modeled as a multi-label
classification problem. Typically, crack pixels are much fewer than non-crack
pixels. To deal with the problem with severely imbalanced data, a strategy with
modifying the ratio of positive to negative samples is proposed. The method is
tested on two public databases and compared with five existing methods.
Experimental results show that it outperforms the other methods.
Energy Propagation in Deep Convolutional Neural Networks
Authors: Thomas Wiatowski, Philipp Grohs, Helmut Bölcskei
Date: 2017-04-12 00:00:00

Many practical machine learning tasks employ very deep convolutional neural
networks. Such large depths pose formidable computational challenges in
training and operating the network. It is therefore important to understand how
fast the energy contained in the propagated signals (a.k.a. feature maps)
decays across layers. In addition, it is desirable that the feature extractor
generated by the network be informative in the sense of the only signal mapping
to the all-zeros feature vector being the zero input signal. This "trivial
null-set" property can be accomplished by asking for "energy conservation" in
the sense of the energy in the feature vector being proportional to that of the
corresponding input signal. This paper establishes conditions for energy
conservation (and thus for a trivial null-set) for a wide class of deep
convolutional neural network-based feature extractors and characterizes
corresponding feature map energy decay rates. Specifically, we consider general
scattering networks employing the modulus non-linearity and we find that under
mild analyticity and high-pass conditions on the filters (which encompass,
inter alia, various constructions of Weyl-Heisenberg filters, wavelets,
ridgelets, ($\alpha$)-curvelets, and shearlets) the feature map energy decays
at least polynomially fast. For broad families of wavelets and Weyl-Heisenberg
filters, the guaranteed decay rate is shown to be exponential. Moreover, we
provide handy estimates of the number of layers needed to have at least
$((1-\varepsilon)\cdot 100)\%$ of the input signal energy be contained in the
feature vector.
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations
Authors: Caglar Aytekin, Xingyang Ni, Francesco Cricri, Emre Aksu
Date: 2018-02-01 00:00:00
Tasks: Anomaly Detection, Clustering, Unsupervised Anomaly Detection

Clustering is essential to many tasks in pattern recognition and computer
vision. With the advent of deep learning, there is an increasing interest in
learning deep unsupervised representations for clustering analysis. Many works
on this domain rely on variants of auto-encoders and use the encoder outputs as
representations/features for clustering. In this paper, we show that an l2
normalization constraint on these representations during auto-encoder training,
makes the representations more separable and compact in the Euclidean space
after training. This greatly improves the clustering accuracy when k-means
clustering is employed on the representations. We also propose a clustering
based unsupervised anomaly detection method using l2 normalized deep
auto-encoder representations. We show the effect of l2 normalization on anomaly
detection accuracy. We further show that the proposed anomaly detection method
greatly improves accuracy compared to previously proposed deep methods such as
reconstruction error based anomaly detection.
Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely Regularized Optimization
Authors: Tianyi Lin, Linbo Qiao, Teng Zhang, Jiashi Feng, Bofeng Zhang
Date: 2017-08-20 00:00:00
Tasks: regression

We consider a wide range of regularized stochastic minimization problems with
two regularization terms, one of which is composed with a linear function. This
optimization model abstracts a number of important applications in artificial
intelligence and machine learning, such as fused Lasso, fused logistic
regression, and a class of graph-guided regularized minimization. The
computational challenges of this model are in two folds. On one hand, the
closed-form solution of the proximal mapping associated with the composed
regularization term or the expected objective function is not available. On the
other hand, the calculation of the full gradient of the expectation in the
objective is very expensive when the number of input data samples is
considerably large. To address these issues, we propose a stochastic variant of
extra-gradient type methods, namely \textsf{Stochastic Primal-Dual Proximal
ExtraGradient descent (SPDPEG)}, and analyze its convergence property for both
convex and strongly convex objectives. For general convex objectives, the
uniformly average iterates generated by \textsf{SPDPEG} converge in expectation
with $O(1/\sqrt{t})$ rate. While for strongly convex objectives, the uniformly
and non-uniformly average iterates generated by \textsf{SPDPEG} converge with
$O(\log(t)/t)$ and $O(1/t)$ rates, respectively. The order of the rate of the
proposed algorithm is known to match the best convergence rate for first-order
stochastic algorithms. Experiments on fused logistic regression and
graph-guided regularized logistic regression problems show that the proposed
algorithm performs very efficiently and consistently outperforms other
competing algorithms.
A Note on Topology Preservation in Classification, and the Construction of a Universal Neuron Grid
Authors: Dietmar Volz
Date: 2013-08-07 00:00:00
Tasks: General Classification

It will be shown that according to theorems of K. Menger, every neuron grid
if identified with a curve is able to preserve the adopted qualitative
structure of a data space. Furthermore, if this identification is made, the
neuron grid structure can always be mapped to a subset of a universal neuron
grid which is constructable in three space dimensions. Conclusions will be
drawn for established neuron grid types as well as neural fields.
Machine learning and evolutionary techniques in interplanetary trajectory design
Authors: Dario Izzo, Christopher Sprague, Dharmesh Tailor
Date: 2018-02-01 00:00:00
Tasks: BIG-bench Machine Learning, Trajectory Planning

After providing a brief historical overview on the synergies between
artificial intelligence research, in the areas of evolutionary computations and
machine learning, and the optimal design of interplanetary trajectories, we
propose and study the use of deep artificial neural networks to represent,
on-board, the optimal guidance profile of an interplanetary mission. The
results, limited to the chosen test case of an Earth-Mars orbital transfer,
extend the findings made previously for landing scenarios and quadcopter
dynamics, opening a new research area in interplanetary trajectory planning.
Full Image Recover for Block-Based Compressive Sensing
Authors: Xuemei Xie, Chenye Wang, Jiang Du, Guangming Shi
Date: 2018-02-01 00:00:00
Tasks: Compressive Sensing

Recent years, compressive sensing (CS) has improved greatly for the
application of deep learning technology. For convenience, the input image is
usually measured and reconstructed block by block. This usually causes block
effect in reconstructed images. In this paper, we present a novel CNN-based
network to solve this problem. In measurement part, the input image is
adaptively measured block by block to acquire a group of measurements. While in
reconstruction part, all the measurements from one image are used to
reconstruct the full image at the same time. Different from previous method
recovering block by block, the structure information destroyed in measurement
part is recovered in our framework. Block effect is removed accordingly. We
train the proposed framework by mean square error (MSE) loss function.
Experiments show that there is no block effect at all in the proposed method.
And our results outperform 1.8 dB compared with existing methods.
Perceptual Compressive Sensing
Authors: Jiang Du, Xuemei Xie, Chenye Wang, Guangming Shi
Date: 2018-02-01 00:00:00
Tasks: Compressive Sensing

Compressive sensing (CS) works to acquire measurements at sub-Nyquist rate
and recover the scene images. Existing CS methods always recover the scene
images in pixel level. This causes the smoothness of recovered images and lack
of structure information, especially at a low measurement rate. To overcome
this drawback, in this paper, we propose perceptual CS to obtain high-level
structured recovery. Our task no longer focuses on pixel level. Instead, we
work to make a better visual effect. In detail, we employ perceptual loss,
defined on feature level, to enhance the structure information of the recovered
images. Experiments show that our method achieves better visual results with
stronger structure information than existing CS methods at the same measurement
rate.
Polar Transformer Networks
Authors: Carlos Esteves, Christine Allen-Blanchette, Xiaowei Zhou, Kostas Daniilidis
Date: 2017-09-06 00:00:00
Proceeding: ICLR 2018 1
Tasks: Rotated MNIST, Translation

Convolutional neural networks (CNNs) are inherently equivariant to
translation. Efforts to embed other forms of equivariance have concentrated
solely on rotation. We expand the notion of equivariance in CNNs through the
Polar Transformer Network (PTN). PTN combines ideas from the Spatial
Transformer Network (STN) and canonical coordinate representations. The result
is a network invariant to translation and equivariant to both rotation and
scale. PTN is trained end-to-end and composed of three distinct stages: a polar
origin predictor, the newly introduced polar transformer module and a
classifier. PTN achieves state-of-the-art on rotated MNIST and the newly
introduced SIM2MNIST dataset, an MNIST variation obtained by adding clutter and
perturbing digits with translation, rotation and scaling. The ideas of PTN are
extensible to 3D which we demonstrate through the Cylindrical Transformer
Network.
On the Iteration Complexity Analysis of Stochastic Primal-Dual Hybrid Gradient Approach with High Probability
Authors: Linbo Qiao, Tianyi Lin, Qi Qin, Xicheng Lu
Date: 2018-01-22 00:00:00

In this paper, we propose a stochastic Primal-Dual Hybrid Gradient (PDHG)
approach for solving a wide spectrum of regularized stochastic minimization
problems, where the regularization term is composite with a linear function. It
has been recognized that solving this kind of problem is challenging since the
closed-form solution of the proximal mapping associated with the regularization
term is not available due to the imposed linear composition, and the
per-iteration cost of computing the full gradient of the expected objective
function is extremely high when the number of input data samples is
considerably large.
  Our new approach overcomes these issues by exploring the special structure of
the regularization term and sampling a few data points at each iteration.
Rather than analyzing the convergence in expectation, we provide the detailed
iteration complexity analysis for the cases of both uniformly and non-uniformly
averaged iterates with high probability. This strongly supports the good
practical performance of the proposed approach. Numerical experiments
demonstrate that the efficiency of stochastic PDHG, which outperforms other
competing algorithms, as expected by the high-probability convergence analysis.
Alternating Multi-bit Quantization for Recurrent Neural Networks
Authors: Chen Xu, Jianqiang Yao, Zhouchen Lin, Wenwu Ou, Yuanbin Cao, Zhirong Wang, Hongbin Zha
Date: 2018-02-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: image-classification, Image Classification, Quantization

Recurrent neural networks have achieved excellent performance in many
applications. However, on portable devices with limited resources, the models
are often too large to deploy. For applications on the server with large scale
concurrent requests, the latency during inference can also be very critical for
costly computing resources. In this work, we address these problems by
quantizing the network, both weights and activations, into multiple binary
codes {-1,+1}. We formulate the quantization as an optimization problem. Under
the key observation that once the quantization coefficients are fixed the
binary codes can be derived efficiently by binary search tree, alternating
minimization is then applied. We test the quantization for two well-known RNNs,
i.e., long short term memory (LSTM) and gated recurrent unit (GRU), on the
language models. Compared with the full-precision counter part, by 2-bit
quantization we can achieve ~16x memory saving and ~6x real inference
acceleration on CPUs, with only a reasonable loss in the accuracy. By 3-bit
quantization, we can achieve almost no loss in the accuracy or even surpass the
original model, with ~10.5x memory saving and ~3x real inference acceleration.
Both results beat the exiting quantization works with large margins. We extend
our alternating quantization to image classification tasks. In both RNNs and
feedforward neural networks, the method also achieves excellent performance.
Distributed Newton Methods for Deep Neural Networks
Authors: Chien-Chih Wang, Kent Loong Tan, Chun-Ting Chen, Yu-Hsiang Lin, S. Sathiya Keerthi, Dhruv Mahajan, S. Sundararajan, Chih-Jen Lin
Date: 2018-02-01 00:00:00

Deep learning involves a difficult non-convex optimization problem with a
large number of weights between any two adjacent layers of a deep structure. To
handle large data sets or complicated networks, distributed training is needed,
but the calculation of function, gradient, and Hessian is expensive. In
particular, the communication and the synchronization cost may become a
bottleneck. In this paper, we focus on situations where the model is
distributedly stored, and propose a novel distributed Newton method for
training deep neural networks. By variable and feature-wise data partitions,
and some careful designs, we are able to explicitly use the Jacobian matrix for
matrix-vector products in the Newton method. Some techniques are incorporated
to reduce the running time as well as the memory consumption. First, to reduce
the communication cost, we propose a diagonalization method such that an
approximate Newton direction can be obtained without communication between
machines. Second, we consider subsampled Gauss-Newton matrices for reducing the
running time as well as the communication cost. Third, to reduce the
synchronization cost, we terminate the process of finding an approximate Newton
direction even though some nodes have not finished their tasks. Details of some
implementation issues in distributed environments are thoroughly investigated.
Experiments demonstrate that the proposed method is effective for the
distributed training of deep neural networks. In compared with stochastic
gradient methods, it is more robust and may give better test accuracy.
A Modified Sigma-Pi-Sigma Neural Network with Adaptive Choice of Multinomials
Authors: Feng Li, Yan Liu, Khidir Shaib Mohamed, Wei Wu
Date: 2018-02-01 00:00:00

Sigma-Pi-Sigma neural networks (SPSNNs) as a kind of high-order neural
networks can provide more powerful mapping capability than the traditional
feedforward neural networks (Sigma-Sigma neural networks). In the existing
literature, in order to reduce the number of the Pi nodes in the Pi layer, a
special multinomial P_s is used in SPSNNs. Each monomial in P_s is linear with
respect to each particular variable sigma_i when the other variables are taken
as constants. Therefore, the monomials like sigma_i^n or sigma_i^n sigma_j with
n>1 are not included. This choice may be somehow intuitive, but is not
necessarily the best. We propose in this paper a modified Sigma-Pi-Sigma neural
network (MSPSNN) with an adaptive approach to find a better multinomial for a
given problem. To elaborate, we start from a complete multinomial with a given
order. Then we employ a regularization technique in the learning process for
the given problem to reduce the number of monomials used in the multinomial,
and end up with a new SPSNN involving the same number of monomials (= the
number of nodes in the Pi-layer) as in P_s. Numerical experiments on some
benchmark problems show that our MSPSNN behaves better than the traditional
SPSNN with P_s.
Interpreting CNNs via Decision Trees
Authors: Quanshi Zhang, Yu Yang, Haotian Ma, Ying Nian Wu
Date: 2018-02-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, Prediction

This paper aims to quantitatively explain rationales of each prediction that
is made by a pre-trained convolutional neural network (CNN). We propose to
learn a decision tree, which clarifies the specific reason for each prediction
made by the CNN at the semantic level. I.e., the decision tree decomposes
feature representations in high conv-layers of the CNN into elementary concepts
of object parts. In this way, the decision tree tells people which object parts
activate which filters for the prediction and how much they contribute to the
prediction score. Such semantic and quantitative explanations for CNN
predictions have specific values beyond the traditional pixel-level analysis of
CNNs. More specifically, our method mines all potential decision modes of the
CNN, where each mode represents a common case of how the CNN uses object parts
for prediction. The decision tree organizes all potential decision modes in a
coarse-to-fine manner to explain CNN predictions at different fine-grained
levels. Experiments have demonstrated the effectiveness of the proposed method.
Approximations in the homogeneous Ising model
Authors: Alejandro Murua-Sazo, Ranjan Maitra
Date: 2017-12-06 00:00:00
Tasks: Bayesian Inference, model

The Ising model is important in statistical modeling and inference in many applications, however its normalizing constant, mean number of active vertices and mean spin interaction -- quantities needed in inference -- are computationally intractable. We provide accurate approximations that make it possible to numerically calculate these quantities in the homogeneous case. Simulation studies indicate good performance of our approximation formulae that are scalable and unfazed by the size (number of nodes, degree of graph) of the Markov Random Field. The practical import of our approximation formulae is illustrated in performing Bayesian inference in a functional Magnetic Resonance Imaging activation detection experiment, and also in likelihood ratio testing for anisotropy in the spatial patterns of yearly increases in pistachio tree yields.