pwc-archive/papers-with-abstracts huggingface dataset:
Finite Sample Analysis of Approximate Message Passing Algorithms
Authors: Cynthia Rush, Ramji Venkataramanan
Date: 2016-06-06 00:00:00
Tasks: compressed sensing

Approximate message passing (AMP) refers to a class of efficient algorithms
for statistical estimation in high-dimensional problems such as compressed
sensing and low-rank matrix estimation. This paper analyzes the performance of
AMP in the regime where the problem dimension is large but finite. For
concreteness, we consider the setting of high-dimensional regression, where the
goal is to estimate a high-dimensional vector $\beta_0$ from a noisy
measurement $y=A \beta_0 + w$. AMP is a low-complexity, scalable algorithm for
this problem. Under suitable assumptions on the measurement matrix $A$, AMP has
the attractive feature that its performance can be accurately characterized in
the large system limit by a simple scalar iteration called state evolution.
Previous proofs of the validity of state evolution have all been asymptotic
convergence results. In this paper, we derive a concentration inequality for
AMP with i.i.d. Gaussian measurement matrices with finite size $n \times N$.
The result shows that the probability of deviation from the state evolution
prediction falls exponentially in $n$. This provides theoretical support for
empirical findings that have demonstrated excellent agreement of AMP
performance with state evolution predictions for moderately large dimensions.
The concentration inequality also indicates that the number of AMP iterations
$t$ can grow no faster than order $\frac{\log n}{\log \log n}$ for the
performance to be close to the state evolution predictions with high
probability. The analysis can be extended to obtain similar non-asymptotic
results for AMP in other settings such as low-rank matrix estimation.
Patchwise object tracking via structural local sparse appearance model
Authors: Hossein Kashiyani, Shahriar B. Shokouhi
Date: 2018-03-16 00:00:00
Tasks: Object, Object Tracking, Position, Visual Tracking

In this paper, we propose a robust visual tracking method which exploits the
relationships of targets in adjacent frames using patchwise joint sparse
representation. Two sets of overlapping patches with different sizes are
extracted from target candidates to construct two dictionaries with
consideration of joint sparse representation. By applying this representation
into structural sparse appearance model, we can take two-fold advantages.
First, the correlation of target patches over time is considered. Second, using
this local appearance model with different patch sizes takes into account local
features of target thoroughly. Furthermore, the position of candidate patches
and their occlusion levels are utilized simultaneously to obtain the final
likelihood of target candidates. Evaluations on recent challenging benchmark
show that our tracking method outperforms the state-of-the-art trackers.
Deep Learning Logo Detection with Data Expansion by Synthesising Context
Authors: Hang Su, Xiatian Zhu, Shaogang Gong
Date: 2016-12-29 00:00:00
Tasks: Benchmarking, Deep Learning

Logo detection in unconstrained images is challenging, particularly when only
very sparse labelled training images are accessible due to high labelling
costs. In this work, we describe a model training image synthesising method
capable of improving significantly logo detection performance when only a
handful of (e.g., 10) labelled training images captured in realistic context
are available, avoiding extensive manual labelling costs. Specifically, we
design a novel algorithm for generating Synthetic Context Logo (SCL) training
images to increase model robustness against unknown background clutters,
resulting in superior logo detection performance. For benchmarking model
performance, we introduce a new logo detection dataset TopLogo-10 collected
from top 10 most popular clothing/wearable brandname logos captured in rich
visual context. Extensive comparisons show the advantages of our proposed SCL
model over the state-of-the-art alternatives for logo detection using two
real-world logo benchmark datasets: FlickrLogo-32 and our new TopLogo-10.
Towards Image Understanding from Deep Compression without Decoding
Authors: Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc van Gool
Date: 2018-03-16 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, General Classification, Image Compression, Segmentation

Motivated by recent work on deep neural network (DNN)-based image compression
methods showing potential improvements in image quality, savings in storage,
and bandwidth reduction, we propose to perform image understanding tasks such
as classification and segmentation directly on the compressed representations
produced by these compression methods. Since the encoders and decoders in
DNN-based compression methods are neural networks with feature-maps as internal
representations of the images, we directly integrate these with architectures
for image understanding. This bypasses decoding of the compressed
representation into RGB space and reduces computational cost. Our study shows
that accuracies comparable to networks that operate on compressed RGB images
can be achieved while reducing the computational complexity up to $2\times$.
Furthermore, we show that synergies are obtained by jointly training
compression networks with classification networks on the compressed
representations, improving image quality, classification accuracy, and
segmentation performance. We find that inference from compressed
representations is particularly advantageous compared to inference from
compressed RGB images for aggressive compression rates.
Towards Advanced Phenotypic Mutations in Cartesian Genetic Programming
Authors: Roman Kalkreuth
Date: 2018-03-16 00:00:00
Tasks: regression, Symbolic Regression

Cartesian Genetic Programming is often used with a point mutation as the sole
genetic operator. In this paper, we propose two phenotypic mutation techniques
and take a step towards advanced phenotypic mutations in Cartesian Genetic
Programming. The functionality of the proposed mutations is inspired by
biological evolution which mutates DNA sequences by inserting and deleting
nucleotides. Experiments with symbolic regression and boolean functions
problems show a better search performance when the proposed mutations are in
use. The results of our experiments indicate that the use of phenotypic
mutations could be beneficial for the use of Cartesian Genetic Programming.
Learning Sparse Deep Feedforward Networks via Tree Skeleton Expansion
Authors: Zhourong Chen, Xiaopeng Li, Nevin L. Zhang
Date: 2018-03-16 00:00:00

Despite the popularity of deep learning, structure learning for deep models
remains a relatively under-explored area. In contrast, structure learning has
been studied extensively for probabilistic graphical models (PGMs). In
particular, an efficient algorithm has been developed for learning a class of
tree-structured PGMs called hierarchical latent tree models (HLTMs), where
there is a layer of observed variables at the bottom and multiple layers of
latent variables on top. In this paper, we propose a simple method for learning
the structures of feedforward neural networks (FNNs) based on HLTMs. The idea
is to expand the connections in the tree skeletons from HLTMs and to use the
resulting structures for FNNs. An important characteristic of FNN structures
learned this way is that they are sparse. We present extensive empirical
results to show that, compared with standard FNNs tuned-manually, sparse FNNs
learned by our method achieve better or comparable classification performance
with much fewer parameters. They are also more interpretable.
Gaussian Processes indexed on the symmetric group: prediction and learning
Authors: François Bachoc, Baptiste Broto, Fabrice Gamboa, Jean-Michel Loubes
Date: 2018-03-16 00:00:00
Tasks: Gaussian Processes

In the framework of the supervised learning of a real function defined on a space X , the so called Kriging method stands on a real Gaussian field defined on X. The Euclidean case is well known and has been widely studied. In this paper, we explore the less classical case where X is the non commutative finite group of permutations. In this setting, we propose and study an harmonic analysis of the covariance operators that enables to consider Gaussian processes models and forecasting issues. Our theory is motivated by statistical ranking problems.
The Perception-Distortion Tradeoff
Authors: Yochai Blau, Tomer Michaeli
Date: 2017-11-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Restoration, SSIM, Super-Resolution

Image restoration algorithms are typically evaluated by some distortion measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this paper, we prove mathematically that distortion and perceptual quality are at odds with each other. Specifically, we study the optimal probability for correctly discriminating the outputs of an image restoration algorithm from real images. We show that as the mean distortion decreases, this probability must increase (indicating worse perceptual quality). As opposed to the common belief, this result holds true for any distortion measure, and is not only a problem of the PSNR or SSIM criteria. We also show that generative-adversarial-nets (GANs) provide a principled way to approach the perception-distortion bound. This constitutes theoretical support to their observed success in low-level vision tasks. Based on our analysis, we propose a new methodology for evaluating image restoration methods, and use it to perform an extensive comparison between recent super-resolution algorithms.
Vulnerability of Deep Learning
Authors: Richard Kenway
Date: 2018-03-16 00:00:00
Tasks: Adversarial Attack, Deep Learning, General Classification, Sensitivity

The Renormalisation Group (RG) provides a framework in which it is possible
to assess whether a deep-learning network is sensitive to small changes in the
input data and hence prone to error, or susceptible to adversarial attack.
Distinct classification outputs are associated with different RG fixed points
and sensitivity to small changes in the input data is due to the presence of
relevant operators at a fixed point. A numerical scheme, based on Monte Carlo
RG ideas, is proposed for identifying the existence of relevant operators and
the corresponding directions of greatest sensitivity in the input data. Thus, a
trained deep-learning network may be tested for its robustness and, if it is
vulnerable to attack, dangerous perturbations of the input data identified.
A dependency look at the reality of constituency
Authors: Xinying Chen, Carlos Gómez-Rodríguez, Ramon Ferrer-i-Cancho
Date: 2017-08-24 00:00:00
Tasks: Sentence

A comment on "Neurophysiological dynamics of phrase-structure building during
sentence processing" by Nelson et al (2017), Proceedings of the National
Academy of Sciences USA 114(18), E3669-E3678.
Lipschitz Constrained GANs via Boundedness and Continuity
Authors: Kanglin Liu, Guoping Qiu
Date: 2018-03-16 00:00:00

One of the challenges in the study of Generative Adversarial Networks (GANs) is the difficulty of its performance control. Lipschitz constraint is essential in guaranteeing training stability for GANs. Although heuristic methods such as weight clipping, gradient penalty and spectral normalization have been proposed to enforce Lipschitz constraint, it is still difficult to achieve a solution that is both practically effective and theoretically provably satisfying a Lipschitz constraint. In this paper, we introduce the boundedness and continuity ($BC$) conditions to enforce the Lipschitz constraint on the discriminator functions of GANs. We prove theoretically that GANs with discriminators meeting the BC conditions satisfy the Lipschitz constraint. We present a practically very effective implementation of a GAN based on a convolutional neural network (CNN) by forcing the CNN to satisfy the $BC$ conditions (BC-GAN). We show that as compared to recent techniques including gradient penalty and spectral normalization, BC-GANs not only have better performances but also lower computational complexity.
M$^2$S-Net: Multi-Modal Similarity Metric Learning based Deep Convolutional Network for Answer Selection
Authors: Lingxun Meng, Yan Li
Date: 2016-04-19 00:00:00
Tasks: Answer Selection, Metric Learning, Sentence

Recent works using artificial neural networks based on distributed word
representation greatly boost performance on various natural language processing
tasks, especially the answer selection problem. Nevertheless, most of the
previous works used deep learning methods (like LSTM-RNN, CNN, etc.) only to
capture semantic representation of each sentence separately, without
considering the interdependence between each other. In this paper, we propose a
novel end-to-end learning framework which constitutes deep convolutional neural
network based on multi-modal similarity metric learning (M$^2$S-Net) on
pairwise tokens. The proposed model demonstrates its performance by surpassing
previous state-of-the-art systems on the answer selection benchmark, i.e.,
TREC-QA dataset, in both MAP and MRR metrics.
Deep Stochastic Configuration Networks with Universal Approximation Property
Authors: Dianhui Wang, Ming Li
Date: 2017-02-18 00:00:00

This paper develops a randomized approach for incrementally building deep
neural networks, where a supervisory mechanism is proposed to constrain the
random assignment of the weights and biases, and all the hidden layers have
direct links to the output layer. A fundamental result on the universal
approximation property is established for such a class of randomized leaner
models, namely deep stochastic configuration networks (DeepSCNs). A learning
algorithm is presented to implement DeepSCNs with either specific architecture
or self-organization. The read-out weights attached with all direct links from
each hidden layer to the output layer are evaluated by the least squares
method. Given a set of training examples, DeepSCNs can speedily produce a
learning representation, that is, a collection of random basis functions with
the cascaded inputs together with the read-out weights. An empirical study on a
function approximation is carried out to demonstrate some properties of the
proposed deep learner model.
Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool
Authors: Feng Liu, Tao Xiang, Timothy M. Hospedales, Wankou Yang, Changyin Sun
Date: 2018-03-16 00:00:00
Tasks: Question Answering, Reinforcement Learning, Visual Question Answering, Visual Question Answering (VQA)

In recent years, visual question answering (VQA) has become topical. The
premise of VQA's significance as a benchmark in AI, is that both the image and
textual question need to be well understood and mutually grounded in order to
infer the correct answer. However, current VQA models perhaps `understand' less
than initially hoped, and instead master the easier task of exploiting cues
given away in the question and biases in the answer distribution. In this paper
we propose the inverse problem of VQA (iVQA). The iVQA task is to generate a
question that corresponds to a given image and answer pair. We propose a
variational iVQA model that can generate diverse, grammatically correct and
content correlated questions that match the given answer. Based on this model,
we show that iVQA is an interesting benchmark for visuo-linguistic
understanding, and a more challenging alternative to VQA because an iVQA model
needs to understand the image better to be successful. As a second
contribution, we show how to use iVQA in a novel reinforcement learning
framework to diagnose any existing VQA model by way of exposing its belief set:
the set of question-answer pairs that the VQA model would predict true for a
given image. This provides a completely new window into what VQA models
`believe' about images. We show that existing VQA models have more erroneous
beliefs than previously thought, revealing their intrinsic weaknesses.
Suggestions are then made on how to address these weaknesses going forward.
Deep Semantic Face Deblurring
Authors: Ziyi Shen, Wei-Sheng Lai, Tingfa Xu, Jan Kautz, Ming-Hsuan Yang
Date: 2018-03-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: Deblurring, Face Recognition

In this paper, we present an effective and efficient face deblurring
algorithm by exploiting semantic cues via deep convolutional neural networks
(CNNs). As face images are highly structured and share several key semantic
components (e.g., eyes and mouths), the semantic information of a face provides
a strong prior for restoration. As such, we propose to incorporate global
semantic priors as input and impose local structure losses to regularize the
output within a multi-scale deep CNN. We train the network with perceptual and
adversarial losses to generate photo-realistic results and develop an
incremental training strategy to handle random blur kernels in the wild.
Quantitative and qualitative evaluations demonstrate that the proposed face
deblurring algorithm restores sharp images with more facial details and
performs favorably against state-of-the-art methods in terms of restoration
quality, face recognition and execution speed.
iVQA: Inverse Visual Question Answering
Authors: Feng Liu, Tao Xiang, Timothy M. Hospedales, Wankou Yang, Changyin Sun
Date: 2017-10-10 00:00:00
Proceeding: CVPR 2018 6
Tasks: Question Answering, Question Generation, Question-Generation, Visual Question Answering, Visual Question Answering (VQA)

We propose the inverse problem of Visual question answering (iVQA), and
explore its suitability as a benchmark for visuo-linguistic understanding. The
iVQA task is to generate a question that corresponds to a given image and
answer pair. Since the answers are less informative than the questions, and the
questions have less learnable bias, an iVQA model needs to better understand
the image to be successful than a VQA model. We pose question generation as a
multi-modal dynamic inference process and propose an iVQA model that can
gradually adjust its focus of attention guided by both a partially generated
question and the answer. For evaluation, apart from existing linguistic
metrics, we propose a new ranking metric. This metric compares the ground truth
question's rank among a list of distractors, which allows the drawbacks of
different algorithms and sources of error to be studied. Experimental results
show that our model can generate diverse, grammatically correct and content
correlated questions that match the given answer.
A Dataset and Architecture for Visual Reasoning with a Working Memory
Authors: Guangyu Robert Yang, Igor Ganichev, Xiao-Jing Wang, Jonathon Shlens, David Sussillo
Date: 2018-03-16 00:00:00
Proceeding: ECCV 2018 9
Tasks: Diagnostic, Logical Reasoning, Visual Question Answering (VQA), Visual Reasoning

A vexing problem in artificial intelligence is reasoning about events that
occur in complex, changing visual stimuli such as in video analysis or game
play. Inspired by a rich tradition of visual reasoning and memory in cognitive
psychology and neuroscience, we developed an artificial, configurable visual
question and answer dataset (COG) to parallel experiments in humans and
animals. COG is much simpler than the general problem of video analysis, yet it
addresses many of the problems relating to visual and logical reasoning and
memory -- problems that remain challenging for modern deep learning
architectures. We additionally propose a deep learning architecture that
performs competitively on other diagnostic VQA datasets (i.e. CLEVR) as well as
easy settings of the COG dataset. However, several settings of COG result in
datasets that are progressively more challenging to learn. After training, the
network can zero-shot generalize to many new tasks. Preliminary analyses of the
network architectures trained on COG demonstrate that the network accomplishes
the task in a manner interpretable to humans.
Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground
Authors: Deng-Ping Fan, Ming-Ming Cheng, Jiang-Jiang Liu, Shang-Hua Gao, Qibin Hou, Ali Borji
Date: 2018-03-16 00:00:00
Proceeding: ECCV 2018 9
Tasks: Attribute, Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

We provide a comprehensive evaluation of salient object detection (SOD)
models. Our analysis identifies a serious design bias of existing SOD datasets
which assumes that each image contains at least one clearly outstanding salient
object in low clutter. The design bias has led to a saturated high performance
for state-of-the-art SOD models when evaluated on existing datasets. The
models, however, still perform far from being satisfactory when applied to
real-world daily scenes. Based on our analyses, we first identify 7 crucial
aspects that a comprehensive and balanced dataset should fulfill. Then, we
propose a new high quality dataset and update the previous saliency benchmark.
Specifically, our SOC (Salient Objects in Clutter) dataset, includes images
with salient and non-salient objects from daily object categories. Beyond
object category annotations, each salient image is accompanied by attributes
that reflect common challenges in real-world scenes. Finally, we report
attribute-based performance assessment on our dataset.
A Kernel Theory of Modern Data Augmentation
Authors: Tri Dao, Albert Gu, Alexander J. Ratner, Virginia Smith, Christopher De Sa, Christopher Ré
Date: 2018-03-16 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation

Data augmentation, a technique in which a training set is expanded with
class-preserving transformations, is ubiquitous in modern machine learning
pipelines. In this paper, we seek to establish a theoretical framework for
understanding data augmentation. We approach this from two directions: First,
we provide a general model of augmentation as a Markov process, and show that
kernels appear naturally with respect to this model, even when we do not employ
kernel classification. Next, we analyze more directly the effect of
augmentation on kernel classifiers, showing that data augmentation can be
approximated by first-order feature averaging and second-order variance
regularization components. These frameworks both serve to illustrate the ways
in which data augmentation affects the downstream learning model, and the
resulting analyses provide novel connections between prior work in invariant
kernels, tangent propagation, and robust optimization. Finally, we provide
several proof-of-concept applications showing that our theory can be useful for
accelerating machine learning workflows, such as reducing the amount of
computation needed to train using augmented data, and predicting the utility of
a transformation prior to training.
Real-time Detection, Tracking, and Classification of Moving and Stationary Objects using Multiple Fisheye Images
Authors: Iljoo Baek, Albert Davies, Geng Yan, Ragunathan, Rajkumar
Date: 2018-03-16 00:00:00
Tasks: General Classification, Moving Object Detection, object-detection, Object Detection

The ability to detect pedestrians and other moving objects is crucial for an
autonomous vehicle. This must be done in real-time with minimum system
overhead. This paper discusses the implementation of a surround view system to
identify moving as well as static objects that are close to the ego vehicle.
The algorithm works on 4 views captured by fisheye cameras which are merged
into a single frame. The moving object detection and tracking solution uses
minimal system overhead to isolate regions of interest (ROIs) containing moving
objects. These ROIs are then analyzed using a deep neural network (DNN) to
categorize the moving object. With deployment and testing on a real car in
urban environments, we have demonstrated the practical feasibility of the
solution. The video demos of our algorithm have been uploaded to Youtube:
https://youtu.be/vpoCfC724iA, https://youtu.be/2X4aqH2bMBs