pwc-archive/papers-with-abstracts huggingface dataset:
Adaptively Connected Neural Networks
Authors: Guangrun Wang, Keze Wang, Liang Lin
Date: 2019-04-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Document Classification, Image Classification, Person Re-Identification

This paper presents a novel adaptively connected neural network (ACNet) to
improve the traditional convolutional neural networks (CNNs) {in} two aspects.
First, ACNet employs a flexible way to switch global and local inference in
processing the internal feature representations by adaptively determining the
connection status among the feature nodes (e.g., pixels of the feature maps)
\footnote{In a computer vision domain, a node refers to a pixel of a feature
map{, while} in {the} graph domain, a node denotes a graph node.}. We can show
that existing CNNs, the classical multilayer perceptron (MLP), and the recently
proposed non-local network (NLN) \cite{nonlocalnn17} are all special cases of
ACNet. Second, ACNet is also capable of handling non-Euclidean data. Extensive
experimental analyses on {a variety of benchmarks (i.e.,} ImageNet-1k
classification, COCO 2017 detection and segmentation, CUHK03 person
re-identification, CIFAR analysis, and Cora document categorization)
demonstrate that {ACNet} cannot only achieve state-of-the-art performance but
also overcome the limitation of the conventional MLP and CNN
\footnote{Corresponding author: Liang Lin (linliang@ieee.org)}. The code is
available at
\url{https://github.com/wanggrun/Adaptively-Connected-Neural-Networks}.
Spoken Language Intent Detection using Confusion2Vec
Authors: Prashanth Gurunath Shivakumar, Mu Yang, Panayiotis Georgiou
Date: 2019-04-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Intent Detection, speech-recognition, Speech Recognition, Spoken Language Understanding

Decoding speaker's intent is a crucial part of spoken language understanding (SLU). The presence of noise or errors in the text transcriptions, in real life scenarios make the task more challenging. In this paper, we address the spoken language intent detection under noisy conditions imposed by automatic speech recognition (ASR) systems. We propose to employ confusion2vec word feature representation to compensate for the errors made by ASR and to increase the robustness of the SLU system. The confusion2vec, motivated from human speech production and perception, models acoustic relationships between words in addition to the semantic and syntactic relations of words in human language. We hypothesize that ASR often makes errors relating to acoustically similar words, and the confusion2vec with inherent model of acoustic relationships between words is able to compensate for the errors. We demonstrate through experiments on the ATIS benchmark dataset, the robustness of the proposed model to achieve state-of-the-art results under noisy ASR conditions. Our system reduces classification error rate (CER) by 20.84% and improves robustness by 37.48% (lower CER degradation) relative to the previous state-of-the-art going from clean to noisy transcripts. Improvements are also demonstrated when training the intent detection models on noisy transcripts.
A Dilated Inception Network for Visual Saliency Prediction
Authors: Sheng Yang, Guosheng Lin, Qiuping Jiang, Weisi Lin
Date: 2019-04-07 00:00:00
Tasks: Diversity, Prediction, Saliency Prediction, Video Saliency Detection

Recently, with the advent of deep convolutional neural networks (DCNN), the improvements in visual saliency prediction research are impressive. One possible direction to approach the next improvement is to fully characterize the multi-scale saliency-influential factors with a computationally-friendly module in DCNN architectures. In this work, we proposed an end-to-end dilated inception network (DINet) for visual saliency prediction. It captures multi-scale contextual features effectively with very limited extra parameters. Instead of utilizing parallel standard convolutions with different kernel sizes as the existing inception module, our proposed dilated inception module (DIM) uses parallel dilated convolutions with different dilation rates which can significantly reduce the computation load while enriching the diversity of receptive fields in feature maps. Moreover, the performance of our saliency model is further improved by using a set of linear normalization-based probability distribution distance metrics as loss functions. As such, we can formulate saliency prediction as a probability distribution prediction task for global saliency inference instead of a typical pixel-wise regression problem. Experimental results on several challenging saliency benchmark datasets demonstrate that our DINet with proposed loss functions can achieve state-of-the-art performance with shorter inference time.
Image and Video Compression with Neural Networks: A Review
Authors: Siwei Ma, Xinfeng Zhang, Chuanmin Jia, Zhenghui Zhao, Shiqi Wang, Shanshe Wang
Date: 2019-04-07 00:00:00
Tasks: Video Compression

In recent years, the image and video coding technologies have advanced by
leaps and bounds. However, due to the popularization of image and video
acquisition devices, the growth rate of image and video data is far beyond the
improvement of the compression ratio. In particular, it has been widely
recognized that there are increasing challenges of pursuing further coding
performance improvement within the traditional hybrid coding framework. Deep
convolution neural network (CNN) which makes the neural network resurge in
recent years and has achieved great success in both artificial intelligent and
signal processing fields, also provides a novel and promising solution for
image and video compression. In this paper, we provide a systematic,
comprehensive and up-to-date review of neural network based image and video
compression techniques. The evolution and development of neural network based
compression methodologies are introduced for images and video respectively.
More specifically, the cutting-edge video coding techniques by leveraging deep
learning and HEVC framework are presented and discussed, which promote the
state-of-the-art video coding performance substantially. Moreover, the
end-to-end image and video coding frameworks based on neural networks are also
reviewed, revealing interesting explorations on next generation image and video
coding frameworks/standards. The most significant research works on the image
and video coding related topics using neural networks are highlighted, and
future trends are also envisioned. In particular, the joint compression on
semantic and visual information is tentatively explored to formulate high
efficiency signal representation structure for both human vision and machine
vision, which are the two dominant signal receptor in the age of artificial
intelligence.
The Role of Interactivity in Local Differential Privacy
Authors: Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth
Date: 2019-04-07 00:00:00
Tasks: Two-sample testing

We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially interactive protocols may query users adaptively in sequence, but they cannot return to previously queried users. The vast majority of existing lower bounds for local differential privacy apply only to sequentially interactive protocols, and before this paper it was not known whether fully interactive protocols were more powerful. We resolve this question. First, we classify locally private protocols by their compositionality, the multiplicative factor $k \geq 1$ by which the sum of a protocol's single-round privacy parameters exceeds its overall privacy guarantee. We then show how to efficiently transform any fully interactive $k$-compositional protocol into an equivalent sequentially interactive protocol with an $O(k)$ blowup in sample complexity. Next, we show that our reduction is tight by exhibiting a family of problems such that for any $k$, there is a fully interactive $k$-compositional protocol which solves the problem, while no sequentially interactive protocol can solve the problem without at least an $\tilde \Omega(k)$ factor more examples. We then turn our attention to hypothesis testing problems. We show that for a large class of compound hypothesis testing problems --- which include all simple hypothesis testing problems as a special case --- a simple noninteractive test is optimal among the class of all (possibly fully interactive) tests.
Fast Supervised Discrete Hashing
Authors: Jie Gui, Tongliang Liu, Zhenan Sun, DaCheng Tao, Tieniu Tan
Date: 2019-04-07 00:00:00
Tasks: regression

Learning-based hashing algorithms are ``hot topics" because they can greatly
increase the scale at which existing methods operate. In this paper, we propose
a new learning-based hashing method called ``fast supervised discrete hashing"
(FSDH) based on ``supervised discrete hashing" (SDH). Regressing the training
examples (or hash code) to the corresponding class labels is widely used in
ordinary least squares regression. Rather than adopting this method, FSDH uses
a very simple yet effective regression of the class labels of training examples
to the corresponding hash code to accelerate the algorithm. To the best of our
knowledge, this strategy has not previously been used for hashing. Traditional
SDH decomposes the optimization into three sub-problems, with the most critical
sub-problem - discrete optimization for binary hash codes - solved using
iterative discrete cyclic coordinate descent (DCC), which is time-consuming.
However, FSDH has a closed-form solution and only requires a single rather than
iterative hash code-solving step, which is highly efficient. Furthermore, FSDH
is usually faster than SDH for solving the projection matrix for least squares
regression, making FSDH generally faster than SDH. For example, our results
show that FSDH is about 12-times faster than SDH when the number of hashing
bits is 128 on the CIFAR-10 data base, and FSDH is about 151-times faster than
FastHash when the number of hashing bits is 64 on the MNIST data-base. Our
experimental results show that FSDH is not only fast, but also outperforms
other comparative methods.
Place-specific Background Modeling Using Recursive Autoencoders
Authors: Yamaguchi Kousuke, Tanaka Kanji, Sugimoto Takuma, Ide Rino, Takeda Koji
Date: 2019-04-07 00:00:00
Tasks: Anomaly Detection, Change Detection, Image Compression

Image change detection (ICD) to detect changed objects in front of a vehicle
with respect to a place-specific background model using an on-board monocular
vision system is a fundamental problem in intelligent vehicle (IV). From the
perspective of recent large-scale IV applications, it can be impractical in
terms of space/time efficiency to train place-specific background models for
every possible place. To address these issues, we introduce a new autoencoder
(AE) based efficient ICD framework that combines the advantages of AE-based
anomaly detection (AD) and AE-based image compression (IC). We propose a method
that uses AE reconstruction errors as a single unified measure for training a
minimal set of place-specific AEs and maintains detection accuracy. We
introduce an efficient incremental recursive AE (rAE) training framework that
recursively summarizes a large collection of background images into the AE set.
The results of experiments on challenging cross-season ICD tasks validate the
efficacy of the proposed approach.
Scalable Change Retrieval Using Deep 3D Neural Codes
Authors: Kojima Yusuke, Tanaka Kanji, Yang Naiming, Hirota Yuji
Date: 2019-04-07 00:00:00
Tasks: Change Detection, Image Retrieval, Retrieval

We present a novel scalable framework for image change detection (ICD) from
an on-board 3D imagery system. We argue that existing ICD systems are
constrained by the time required to align a given query image with individual
reference image coordinates. We utilize an invariant coordinate system (ICS) to
replace the time-consuming image alignment with an offline pre-processing
procedure. Our key contribution is an extension of the traditional image
comparison-based ICD tasks to setups of the image retrieval (IR) task. We
replace each component of the 3D ICD system, i.e., (1) image modeling, (2)
image alignment, and (3) image differencing, with significantly efficient
variants from the bag-of-words (BoW) IR paradigm. Further, we train a deep 3D
feature extractor in an unsupervised manner using an unsupervised Siamese
network and automatically collected training data. We conducted experiments on
a challenging cross-season ICD task using a publicly available dataset and
thereby validate the efficacy of the proposed approach.
Long-Term Vehicle Localization by Recursive Knowledge Distillation
Authors: Hiroki Tomoe, Tanaka Kanji
Date: 2019-04-07 00:00:00
Tasks: Domain Adaptation, Ensemble Learning, Knowledge Distillation, Memorization, Visual Place Recognition

Most of the current state-of-the-art frameworks for cross-season visual place
recognition (CS-VPR) focus on domain adaptation (DA) to a single specific
season. From the viewpoint of long-term CS-VPR, such frameworks do not scale
well to sequential multiple domains (e.g., spring - summer - autumn - winter -
... ). The goal of this study is to develop a novel long-term ensemble learning
(LEL) framework that allows for a constant cost retraining in long-term
sequential-multi-domain CS-VPR (SMD-VPR), which only requires the memorization
of a small constant number of deep convolutional neural networks (CNNs) and can
retrain the CNN ensemble of every season at a small constant time/space cost.
We frame our task as the multi-teacher multi-student knowledge distillation
(MTMS-KD), which recursively compresses all the previous season's knowledge
into a current CNN ensemble. We further address the issue of
teacher-student-assignment (TSA) to achieve a good
generalization/specialization tradeoff. Experimental results on SMD-VPR tasks
validate the efficacy of the proposed approach.
Supervised Discrete Hashing with Relaxation
Authors: Jie Gui, Tongliang Liu, Zhenan Sun, DaCheng Tao, Tieniu Tan
Date: 2019-04-07 00:00:00
Tasks: General Classification, regression, Retrieval

Data-dependent hashing has recently attracted attention due to being able to
support efficient retrieval and storage of high-dimensional data such as
documents, images, and videos. In this paper, we propose a novel learning-based
hashing method called "Supervised Discrete Hashing with Relaxation" (SDHR)
based on "Supervised Discrete Hashing" (SDH). SDH uses ordinary least squares
regression and traditional zero-one matrix encoding of class label information
as the regression target (code words), thus fixing the regression target. In
SDHR, the regression target is instead optimized. The optimized regression
target matrix satisfies a large margin constraint for correct classification of
each example. Compared with SDH, which uses the traditional zero-one matrix,
SDHR utilizes the learned regression target matrix and, therefore, more
accurately measures the classification error of the regression model and is
more flexible. As expected, SDHR generally outperforms SDH. Experimental
results on two large-scale image datasets (CIFAR-10 and MNIST) and a
large-scale and challenging face dataset (FRGC) demonstrate the effectiveness
and efficiency of SDHR.
Precision Matrix Estimation with Noisy and Missing Data
Authors: Roger Fan, Byoungwook Jang, Yuekai Sun, Shuheng Zhou
Date: 2019-04-07 00:00:00

Estimating conditional dependence graphs and precision matrices are some of
the most common problems in modern statistics and machine learning. When data
are fully observed, penalized maximum likelihood-type estimators have become
standard tools for estimating graphical models under sparsity conditions.
Extensions of these methods to more complex settings where data are
contaminated with additive or multiplicative noise have been developed in
recent years. In these settings, however, the relative performance of different
methods is not well understood and algorithmic gaps still exist. In particular,
in high-dimensional settings these methods require using non-positive
semidefinite matrices as inputs, presenting novel optimization challenges. We
develop an alternating direction method of multipliers (ADMM) algorithm for
these problems, providing a feasible algorithm to estimate precision matrices
with indefinite input and potentially nonconvex penalties. We compare this
method with existing alternative solutions and empirically characterize the
tradeoffs between them. Finally, we use this method to explore the networks
among US senators estimated from voting records data.
Spatio-Temporal Attention Pooling for Audio Scene Classification
Authors: Huy Phan, Oliver Y. Chén, Lam Pham, Philipp Koch, Maarten De Vos, Ian McLoughlin, Alfred Mertins
Date: 2019-04-06 00:00:00
Tasks: Acoustic Scene Classification, Classification, Data Augmentation, General Classification, Scene Classification

Acoustic scenes are rich and redundant in their content. In this work, we present a spatio-temporal attention pooling layer coupled with a convolutional recurrent neural network to learn from patterns that are discriminative while suppressing those that are irrelevant for acoustic scene classification. The convolutional layers in this network learn invariant features from time-frequency input. The bidirectional recurrent layers are then able to encode the temporal dynamics of the resulting convolutional features. Afterwards, a two-dimensional attention mask is formed via the outer product of the spatial and temporal attention vectors learned from two designated attention layers to weigh and pool the recurrent output into a final feature vector for classification. The network is trained with between-class examples generated from between-class data augmentation. Experiments demonstrate that the proposed method not only outperforms a strong convolutional neural network baseline but also sets new state-of-the-art performance on the LITIS Rouen dataset.
On Training Robust PDF Malware Classifiers
Authors: Yizheng Chen, Shiqi Wang, Dongdong She, Suman Jana
Date: 2019-04-06 00:00:00

Although state-of-the-art PDF malware classifiers can be trained with almost perfect test accuracy (99%) and extremely low false positive rate (under 0.1%), it has been shown that even a simple adversary can evade them. A practically useful malware classifier must be robust against evasion attacks. However, achieving such robustness is an extremely challenging task. In this paper, we take the first steps towards training robust PDF malware classifiers with verifiable robustness properties. For instance, a robustness property can enforce that no matter how many pages from benign documents are inserted into a PDF malware, the classifier must still classify it as malicious. We demonstrate how the worst-case behavior of a malware classifier with respect to specific robustness properties can be formally verified. Furthermore, we find that training classifiers that satisfy formally verified robustness properties can increase the evasion cost of unbounded (i.e., not bounded by the robustness properties) attackers by eliminating simple evasion attacks. Specifically, we propose a new distance metric that operates on the PDF tree structure and specify two classes of robustness properties including subtree insertions and deletions. We utilize state-of-the-art verifiably robust training method to build robust PDF malware classifiers. Our results show that, we can achieve 92.27% average verified robust accuracy over three properties, while maintaining 99.74% accuracy and 0.56% false positive rate. With simple robustness properties, our robust model maintains 7% higher robust accuracy than all the baseline models against unrestricted whitebox attacks. Moreover, the state-of-the-art and new adaptive evolutionary attackers need up to 10 times larger $L_0$ feature distance and 21 times more PDF basic mutations (e.g., inserting and deleting objects) to evade our robust model than the baselines.
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization
Authors: Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock, Shoham Sabach
Date: 2019-04-06 00:00:00

Backtracking line-search is an old yet powerful strategy for finding a better step sizes to be used in proximal gradient algorithms. The main principle is to locally find a simple convex upper bound of the objective function, which in turn controls the step size that is used. In case of inertial proximal gradient algorithms, the situation becomes much more difficult and usually leads to very restrictive rules on the extrapolation parameter. In this paper, we show that the extrapolation parameter can be controlled by locally finding also a simple concave lower bound of the objective function. This gives rise to a double convex-concave backtracking procedure which allows for an adaptive choice of both the step size and extrapolation parameters. We apply this procedure to the class of inertial Bregman proximal gradient methods, and prove that any sequence generated by these algorithms converges globally to a critical point of the function at hand. Numerical experiments on a number of challenging non-convex problems in image processing and machine learning were conducted and show the power of combining inertial step and double backtracking strategy in achieving improved performances.
Randomised Bayesian Least-Squares Policy Iteration
Authors: Nikolaos Tziortziotis, Christos Dimitrakakis, Michalis Vazirgiannis
Date: 2019-04-06 00:00:00
Tasks: Thompson Sampling

We introduce Bayesian least-squares policy iteration (BLSPI), an off-policy,
model-free, policy iteration algorithm that uses the Bayesian least-squares
temporal-difference (BLSTD) learning algorithm to evaluate policies. An online
variant of BLSPI has been also proposed, called randomised BLSPI (RBLSPI), that
improves its policy based on an incomplete policy evaluation step. In online
setting, the exploration-exploitation dilemma should be addressed as we try to
discover the optimal policy by using samples collected by ourselves. RBLSPI
exploits the advantage of BLSTD to quantify our uncertainty about the value
function. Inspired by Thompson sampling, RBLSPI first samples a value function
from a posterior distribution over value functions, and then selects actions
based on the sampled value function. The effectiveness and the exploration
abilities of RBLSPI are demonstrated experimentally in several environments.
Automatic Target Recognition Using Discrimination Based on Optimal Transport
Authors: Ali Sadeghian, Deoksu Lim, Johan Karlsson, Jian Li
Date: 2019-04-06 00:00:00

The use of distances based on optimal transportation has recently shown
promise for discrimination of power spectra. In particular, spectral estimation
methods based on l1 regularization as well as covariance based methods can be
shown to be robust with respect to such distances. These transportation
distances provide a geometric framework where geodesics corresponds to smooth
transition of spectral mass, and have been useful for tracking. In this paper,
we investigate the use of these distances for automatic target recognition. We
study the use of the Monge-Kantorovich distance compared to the standard l2
distance for classifying civilian vehicles based on SAR images. We use a
version of the Monge-Kantorovich distance that applies also for the case where
the spectra may have different total mass, and we formulate the optimization
problem as a minimum flow problem that can be computed using efficient
algorithms.