pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Semantic Deep Hashing
Authors: Sheng Jin
Date: 2018-03-19 00:00:00
Tasks: Deep Hashing, Image Retrieval, Quantization, Retrieval

In recent years, deep hashing methods have been proved to be efficient since
it employs convolutional neural network to learn features and hashing codes
simultaneously. However, these methods are mostly supervised. In real-world
application, it is a time-consuming and overloaded task for annotating a large
number of images. In this paper, we propose a novel unsupervised deep hashing
method for large-scale image retrieval. Our method, namely unsupervised
semantic deep hashing (\textbf{USDH}), uses semantic information preserved in
the CNN feature layer to guide the training of network. We enforce four
criteria on hashing codes learning based on VGG-19 model: 1) preserving
relevant information of feature space in hashing space; 2) minimizing
quantization loss between binary-like codes and hashing codes; 3) improving the
usage of each bit in hashing codes by using maximum information entropy, and 4)
invariant to image rotation. Extensive experiments on CIFAR-10, NUSWIDE have
demonstrated that \textbf{USDH} outperforms several state-of-the-art
unsupervised hashing methods for image retrieval. We also conduct experiments
on Oxford 17 datasets for fine-grained classification to verify its efficiency
for other computer vision tasks.
Transaction Fraud Detection Using GRU-centered Sandwich-structured Model
Authors: Xurui Li, Wei Yu, Tianyu Luwang, Jianbin Zheng, Xuetao Qiu, Jintao Zhao, Lei Xia, Yujiao Li
Date: 2017-11-04 00:00:00
Tasks: Fraud Detection

Rapid growth of modern technologies such as internet and mobile computing are
bringing dramatically increased e-commerce payments, as well as the explosion
in transaction fraud. Meanwhile, fraudsters are continually refining their
tricks, making rule-based fraud detection systems difficult to handle the
ever-changing fraud patterns. Many data mining and artificial intelligence
methods have been proposed for identifying small anomalies in large transaction
data sets, increasing detecting efficiency to some extent. Nevertheless, there
is always a contradiction that most methods are irrelevant to transaction
sequence, yet sequence-related methods usually cannot learn information at
single-transaction level well. In this paper, a new "within->between->within"
sandwich-structured sequence learning architecture has been proposed by
stacking an ensemble method, a deep sequential learning method and another
top-layer ensemble classifier in proper order. Moreover, attention mechanism
has also been introduced in to further improve performance. Models in this
structure have been manifested to be very efficient in scenarios like fraud
detection, where the information sequence is made up of vectors with complex
interconnected features.
Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks
Authors: Seyed Majid Azimi, Peter Fischer, Marco Körner, Peter Reinartz
Date: 2018-03-19 00:00:00
Tasks: Autonomous Driving, Data Augmentation, Management, Semantic Segmentation

The knowledge about the placement and appearance of lane markings is a
prerequisite for the creation of maps with high precision, necessary for
autonomous driving, infrastructure monitoring, lane-wise traffic management,
and urban planning. Lane markings are one of the important components of such
maps. Lane markings convey the rules of roads to drivers. While these rules are
learned by humans, an autonomous driving vehicle should be taught to learn them
to localize itself. Therefore, accurate and reliable lane marking semantic
segmentation in the imagery of roads and highways is needed to achieve such
goals. We use airborne imagery which can capture a large area in a short period
of time by introducing an aerial lane marking dataset. In this work, we propose
a Symmetric Fully Convolutional Neural Network enhanced by Wavelet Transform in
order to automatically carry out lane marking segmentation in aerial imagery.
Due to a heavily unbalanced problem in terms of number of lane marking pixels
compared with background pixels, we use a customized loss function as well as a
new type of data augmentation step. We achieve a very high accuracy in
pixel-wise localization of lane markings without using 3rd-party information.
In this work, we introduce the first high-quality dataset used within our
experiments which contains a broad range of situations and classes of lane
markings representative of current transportation systems. This dataset will be
publicly available and hence, it can be used as the benchmark dataset for
future algorithms within this domain.
A Mixture of Views Network with Applications to the Classification of Breast Microcalcifications
Authors: Yaniv Shachor, Hayit Greenspan, Jacob Goldberger
Date: 2018-03-19 00:00:00
Tasks: General Classification

In this paper we examine data fusion methods for multi-view data
classification. We present a decision concept which explicitly takes into
account the input multi-view structure, where for each case there is a
different subset of relevant views. This data fusion concept, which we dub
Mixture of Views, is implemented by a special purpose neural network
architecture. It is demonstrated on the task of classifying breast
microcalcifications as benign or malignant based on CC and MLO mammography
views. The single view decisions are combined by a data-driven decision,
according to the relevance of each view in a given case, into a global
decision. The method is evaluated on a large multi-view dataset extracted from
the standardized digital database for screening mammography (DDSM). The
experimental results show that our method outperforms previously suggested
fusion methods.
Near-lossless $\ell_\infty$-constrained Image Decompression via Deep Neural Network
Authors: Xi Zhang, Xiaolin Wu
Date: 2018-01-18 00:00:00
Tasks: Image Compression, Image Restoration

Recently a number of CNN-based techniques were proposed to remove image compression artifacts. As in other restoration applications, these techniques all learn a mapping from decompressed patches to the original counterparts under the ubiquitous $\ell_\infty$ metric. However, this approach is incapable of restoring distinctive image details which may be statistical outliers but have high semantic importance (e.g., tiny lesions in medical images). To overcome this weakness, we propose to incorporate an $\ell_\infty$ fidelity criterion in the design of neural network so that no small, distinctive structures of the original image can be dropped or distorted. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in $\ell_\infty$ error metric and perceptual quality, while being competitive in $\ell_2$ error metric as well. It can restore subtle image details that are otherwise destroyed or missed by other algorithms. Our research suggests a new machine learning paradigm of ultra high fidelity image compression that is ideally suited for applications in medicine, space, and sciences.
Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN
Authors: Siyuan Zhao, Zhiwei Xu, Limin Liu, Mengjie Guo
Date: 2017-11-25 00:00:00
Tasks: Deep Learning, Spam detection, text-classification, Text Classification

Nowadays, deep learning has been widely used. In natural language learning,
the analysis of complex semantics has been achieved because of its high degree
of flexibility. The deceptive opinions detection is an important application
area in deep learning model, and related mechanisms have been given attention
and researched. On-line opinions are quite short, varied types and content. In
order to effectively identify deceptive opinions, we need to comprehensively
study the characteristics of deceptive opinions, and explore novel
characteristics besides the textual semantics and emotional polarity that have
been widely used in text analysis. The detection mechanism based on deep
learning has better self-adaptability and can effectively identify all kinds of
deceptive opinions. In this paper, we optimize the convolution neural network
model by embedding the word order characteristics in its convolution layer and
pooling layer, which makes convolution neural network more suitable for various
text classification and deceptive opinions detection. The TensorFlow-based
experiments demonstrate that the detection mechanism proposed in this paper
achieve more accurate deceptive opinion detection results.
Recursive nearest agglomeration (ReNA): fast clustering for approximation of structured signals
Authors: Andrés Hoyos-Idrobo, Gaël Varoquaux, Jonas Kahn, Bertrand Thirion
Date: 2016-09-15 00:00:00
Tasks: Clustering, Denoising, Dimensionality Reduction

In this work, we revisit fast dimension reduction approaches, as with random
projections and random sampling. Our goal is to summarize the data to decrease
computational costs and memory footprint of subsequent analysis. Such dimension
reduction can be very efficient when the signals of interest have a strong
structure, such as with images. We focus on this setting and investigate
feature clustering schemes for data reductions that capture this structure. An
impediment to fast dimension reduction is that good clustering comes with large
algorithmic costs. We address it by contributing a linear-time agglomerative
clustering scheme, Recursive Nearest Agglomeration (ReNA). Unlike existing fast
agglomerative schemes, it avoids the creation of giant clusters. We empirically
validate that it approximates the data as well as traditional
variance-minimizing clustering schemes that have a quadratic complexity. In
addition, we analyze signal approximation with feature clustering and show that
it can remove noise, improving subsequent analysis steps. As a consequence,
data reduction by clustering features with ReNA yields very fast and accurate
models, enabling to process large datasets on budget. Our theoretical analysis
is backed by extensive experiments on publicly-available data that illustrate
the computation efficiency and the denoising properties of the resulting
dimension reduction scheme.
Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision
Authors: Varun Ravi Kumar, Stefan Milz, Martin Simon, Christian Witt, Karl Amende, Johannes Petzold, Senthil Yogamani, Timo Pech
Date: 2018-03-16 00:00:00
Tasks: Depth Estimation, Domain Adaptation

Near field depth estimation around a self driving car is an important
function that can be achieved by four wide angle fisheye cameras having a field
of view of over 180. Depth estimation based on convolutional neural networks
(CNNs) produce state of the art results, but progress is hindered because depth
annotation cannot be obtained manually. Synthetic datasets are commonly used
but they have limitations. For instance, they do not capture the extensive
variability in the appearance of objects like vehicles present in real
datasets. There is also a domain shift while performing inference on natural
images illustrated by many attempts to handle the domain adaptation explicitly.
In this work, we explore an alternate approach of training using sparse LiDAR
data as ground truth for depth estimation for fisheye camera. We built our own
dataset using our self driving car setup which has a 64 beam Velodyne LiDAR and
four wide angle fisheye cameras. To handle the difference in view points of
LiDAR and fisheye camera, an occlusion resolution mechanism was implemented. We
started with Eigen's multiscale convolutional network architecture and improved
by modifying activation function and optimizer. We obtained promising results
on our dataset with RMSE errors comparable to the state of the art results
obtained on KITTI.
Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations
Authors: Yuanzhi Li, Tengyu Ma, Hongyang Zhang
Date: 2017-12-26 00:00:00

We show that the gradient descent algorithm provides an implicit
regularization effect in the learning of over-parameterized matrix
factorization models and one-hidden-layer neural networks with quadratic
activations. Concretely, we show that given $\tilde{O}(dr^{2})$ random linear
measurements of a rank $r$ positive semidefinite matrix $X^{\star}$, we can
recover $X^{\star}$ by parameterizing it by $UU^\top$ with $U\in \mathbb
R^{d\times d}$ and minimizing the squared loss, even if $r \ll d$. We prove
that starting from a small initialization, gradient descent recovers
$X^{\star}$ in $\tilde{O}(\sqrt{r})$ iterations approximately. The results
solve the conjecture of Gunasekar et al.'17 under the restricted isometry
property. The technique can be applied to analyzing neural networks with
one-hidden-layer quadratic activations with some technical modifications.
Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview
Authors: Majd Latah, Levent Toker
Date: 2018-03-19 00:00:00
Tasks: Decision Making, Management

Software defined networking (SDN) represents a promising networking
architecture that combines central management and network programmability. SDN
separates the control plane from the data plane and moves the network
management to a central point, called the controller, that can be programmed
and used as the brain of the network. Recently, the research community has
showed an increased tendency to benefit from the recent advancements in the
artificial intelligence (AI) field to provide learning abilities and better
decision making in SDN. In this study, we provide a detailed overview of the
recent efforts to include AI in SDN. Our study showed that the research efforts
focused on three main sub-fields of AI namely: machine learning,
meta-heuristics and fuzzy inference systems. Accordingly, in this work we
investigate their different application areas and potential use, as well as the
improvements achieved by including AI-based techniques in the SDN paradigm.
Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization
Authors: Yoshiaki Bando, Masato Mimura, Katsutoshi Itoyama, Kazuyoshi Yoshii, Tatsuya Kawahara
Date: 2017-10-31 00:00:00
Tasks: Speech Enhancement

This paper presents a statistical method of single-channel speech enhancement
that uses a variational autoencoder (VAE) as a prior distribution on clean
speech. A standard approach to speech enhancement is to train a deep neural
network (DNN) to take noisy speech as input and output clean speech. Although
this supervised approach requires a very large amount of pair data for
training, it is not robust against unknown environments. Another approach is to
use non-negative matrix factorization (NMF) based on basis spectra trained on
clean speech in advance and those adapted to noise on the fly. This
semi-supervised approach, however, causes considerable signal distortion in
enhanced speech due to the unrealistic assumption that speech spectrograms are
linear combinations of the basis spectra. Replacing the poor linear generative
model of clean speech in NMF with a VAE---a powerful nonlinear deep generative
model---trained on clean speech, we formulate a unified probabilistic
generative model of noisy speech. Given noisy speech as observed data, we can
sample clean speech from its posterior distribution. The proposed method
outperformed the conventional DNN-based method in unseen noisy environments.
Weakly Supervised Object Localization on grocery shelves using simple FCN and Synthetic Dataset
Authors: Srikrishna Varadarajan, Muktabh Mayank Srivastava
Date: 2018-03-19 00:00:00
Tasks: Decoder, image-classification, Image Classification, Object, object-detection, Object Detection, Object Localization, Segmentation, Weakly-Supervised Object Localization

We propose a weakly supervised method using two algorithms to predict object
bounding boxes given only an image classification dataset. First algorithm is a
simple Fully Convolutional Network (FCN) trained to classify object instances.
We use the property of FCN to return a mask for images larger than training
images to get a primary output segmentation mask during test time by passing an
image pyramid to it. We enhance the FCN output mask into final output bounding
boxes by a Convolutional Encoder-Decoder (ConvAE) viz. the second algorithm.
ConvAE is trained to localize objects on an artificially generated dataset of
output segmentation masks. We demonstrate the effectiveness of this method in
localizing objects in grocery shelves where annotating data for object
detection is hard due to variety of objects. This method can be extended to any
problem domain where collecting images of objects is easy and annotating their
coordinates is hard.
Learnable Image Encryption
Authors: Masayuki Tanaka
Date: 2018-03-19 00:00:00
Tasks: BIG-bench Machine Learning

The network-based machine learning algorithm is very powerful tools. However,
it requires huge training dataset. Researchers often meet privacy issues when
they collect image dataset especially for surveillance applications. A
learnable image encryption scheme is introduced. The key idea of this scheme is
to encrypt images, so that human cannot understand images but the network can
be train with encrypted images. This scheme allows us to train the network
without the privacy issues. In this paper, a simple learnable image encryption
algorithm is proposed. Then, the proposed algorithm is validated with cifar
dataset.
Attention-GAN for Object Transfiguration in Wild Images
Authors: Xinyuan Chen, Chang Xu, Xiaokang Yang, DaCheng Tao
Date: 2018-03-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object

This paper studies the object transfiguration problem in wild images. The
generative network in classical GANs for object transfiguration often
undertakes a dual responsibility: to detect the objects of interests and to
convert the object from source domain to target domain. In contrast, we
decompose the generative network into two separat networks, each of which is
only dedicated to one particular sub-task. The attention network predicts
spatial attention maps of images, and the transformation network focuses on
translating objects. Attention maps produced by attention network are
encouraged to be sparse, so that major attention can be paid to objects of
interests. No matter before or after object transfiguration, attention maps
should remain constant. In addition, learning attention network can receive
more instructions, given the available segmentation annotations of images.
Experimental results demonstrate the necessity of investigating attention in
object transfiguration, and that the proposed algorithm can learn accurate
attention to improve quality of generated images.
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
Authors: Guo-Jun Qi
Date: 2017-01-23 00:00:00
Tasks: Generative Adversarial Network, image-classification, Image Classification, Image Generation

In this paper, we present the Lipschitz regularization theory and algorithms
for a novel Loss-Sensitive Generative Adversarial Network (LS-GAN).
Specifically, it trains a loss function to distinguish between real and fake
samples by designated margins, while learning a generator alternately to
produce realistic samples by minimizing their losses. The LS-GAN further
regularizes its loss function with a Lipschitz regularity condition on the
density of real data, yielding a regularized model that can better generalize
to produce new data from a reasonable number of training examples than the
classic GAN. We will further present a Generalized LS-GAN (GLS-GAN) and show it
contains a large family of regularized GAN models, including both LS-GAN and
Wasserstein GAN, as its special cases. Compared with the other GAN models, we
will conduct experiments to show both LS-GAN and GLS-GAN exhibit competitive
ability in generating new images in terms of the Minimum Reconstruction Error
(MRE) assessed on a separate test set. We further extend the LS-GAN to a
conditional form for supervised and semi-supervised learning problems, and
demonstrate its outstanding performance on image classification tasks.
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration
Authors: Xinyuan Zhang, Xin Yuan, Lawrence Carin
Date: 2018-03-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: Compressive Sensing, Image Reconstruction, Image Restoration

Low-rank signal modeling has been widely leveraged to capture non-local
correlation in image processing applications. We propose a new method that
employs low-rank tensor factor analysis for tensors generated by grouped image
patches. The low-rank tensors are fed into the alternative direction multiplier
method (ADMM) to further improve image reconstruction. The motivating
application is compressive sensing (CS), and a deep convolutional architecture
is adopted to approximate the expensive matrix inversion in CS applications. An
iterative algorithm based on this low-rank tensor factorization strategy,
called NLR-TFA, is presented in detail. Experimental results on noiseless and
noisy CS measurements demonstrate the superiority of the proposed approach,
especially at low CS sampling rates.
Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval
Authors: Zheng Zhang, Qin Zou, Yuewei Lin, Long Chen, Song Wang
Date: 2018-03-08 00:00:00
Tasks: Deep Hashing, Image Retrieval, Multi-Label Image Retrieval, Retrieval

Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional feature-learning-based methods. Most of these methods examine the pairwise similarity on the semantic-level labels, where the pairwise similarity is generally defined in a hard-assignment way. That is, the pairwise similarity is '1' if they share no less than one class label and '0' if they do not share any. However, such similarity definition cannot reflect the similarity ranking for pairwise images that hold multiple labels. In this paper, a new deep hashing method is proposed for multi-label image retrieval by re-defining the pairwise similarity into an instance similarity, where the instance similarity is quantified into a percentage based on the normalized semantic labels. Based on the instance similarity, a weighted cross-entropy loss and a minimum mean square error loss are tailored for loss-function construction, and are efficiently used for simultaneous feature learning and hash coding. Experiments on three popular datasets demonstrate that, the proposed method outperforms the competing methods and achieves the state-of-the-art performance in multi-label image retrieval.
Depth-aware CNN for RGB-D Segmentation
Authors: Weiyue Wang, Ulrich Neumann
Date: 2018-03-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Segmentation, Semantic Segmentation, Thermal Image Segmentation

Convolutional neural networks (CNN) are limited by the lack of capability to
handle geometric information due to the fixed grid kernel structure. The
availability of depth data enables progress in RGB-D semantic segmentation with
CNNs. State-of-the-art methods either use depth as additional images or process
spatial information in 3D volumes or point clouds. These methods suffer from
high computation and memory cost. To address these issues, we present
Depth-aware CNN by introducing two intuitive, flexible and effective
operations: depth-aware convolution and depth-aware average pooling. By
leveraging depth similarity between pixels in the process of information
propagation, geometry is seamlessly incorporated into CNN. Without introducing
any additional parameters, both operators can be easily integrated into
existing CNNs. Extensive experiments and ablation studies on challenging RGB-D
semantic segmentation benchmarks validate the effectiveness and flexibility of
our approach.