pwc-archive/papers-with-abstracts huggingface dataset:
Denoising Imaging Polarimetry by an Adapted BM3D Method
Authors: Alexander B. Tibbs, Ilse M. Daly, Nicholas W. Roberts, David R. Bull
Date: 2017-11-13 00:00:00
Tasks: Denoising

Imaging polarimetry allows more information to be extracted from a scene than
conventional intensity or colour imaging. However, a major challenge of imaging
polarimetry is image degradation due to noise. This paper investigates the
mitigation of noise through denoising algorithms and compares existing
denoising algorithms with a new method, based on BM3D. This algorithm, PBM3D,
gives visual quality superior to the state of the art across all images and
noise standard deviations tested. We show that denoising polarization images
using PBM3D allows the degree of polarization to be more accurately calculated
by comparing it to spectroscopy methods.
Random Erasing Data Augmentation
Authors: Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, Yi Yang
Date: 2017-08-16 00:00:00
Tasks: Data Augmentation, General Classification, Image Augmentation, image-classification, Image Classification, object-detection, Object Detection, Person Re-Identification, Robust Object Detection

In this paper, we introduce Random Erasing, a new data augmentation method
for training the convolutional neural network (CNN). In training, Random
Erasing randomly selects a rectangle region in an image and erases its pixels
with random values. In this process, training images with various levels of
occlusion are generated, which reduces the risk of over-fitting and makes the
model robust to occlusion. Random Erasing is parameter learning free, easy to
implement, and can be integrated with most of the CNN-based recognition models.
Albeit simple, Random Erasing is complementary to commonly used data
augmentation techniques such as random cropping and flipping, and yields
consistent improvement over strong baselines in image classification, object
detection and person re-identification. Code is available at:
https://github.com/zhunzhong07/Random-Erasing.
Hindsight policy gradients
Authors: Paulo Rauber, Avinash Ummadisingu, Filipe Mutz, Juergen Schmidhuber
Date: 2017-11-16 00:00:00
Proceeding: ICLR 2019 5
Tasks: Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A reinforcement learning agent that needs to pursue different goals across
episodes requires a goal-conditional policy. In addition to their potential to
generalize desirable behavior to unseen goals, such policies may also enable
higher-level planning based on subgoals. In sparse-reward environments, the
capacity to exploit information about the degree to which an arbitrary goal has
been achieved while another goal was intended appears crucial to enable sample
efficient learning. However, reinforcement learning agents have only recently
been endowed with such capacity for hindsight. In this paper, we demonstrate
how hindsight can be introduced to policy gradient methods, generalizing this
idea to a broad class of successful algorithms. Our experiments on a diverse
selection of sparse-reward environments show that hindsight leads to a
remarkable increase in sample efficiency.
Remedies against the Vocabulary Gap in Information Retrieval
Authors: Christophe Van Gysel
Date: 2017-11-16 00:00:00
Tasks: Information Retrieval, Retrieval

Search engines rely heavily on term-based approaches that represent queries
and documents as bags of words. Text---a document or a query---is represented
by a bag of its words that ignores grammar and word order, but retains word
frequency counts. When presented with a search query, the engine then ranks
documents according to their relevance scores by computing, among other things,
the matching degrees between query and document terms. While term-based
approaches are intuitive and effective in practice, they are based on the
hypothesis that documents that exactly contain the query terms are highly
relevant regardless of query semantics. Inversely, term-based approaches assume
documents that do not contain query terms as irrelevant. However, it is known
that a high matching degree at the term level does not necessarily mean high
relevance and, vice versa, documents that match null query terms may still be
relevant. Consequently, there exists a vocabulary gap between queries and
documents that occurs when both use different words to describe the same
concepts. It is the alleviation of the effect brought forward by this
vocabulary gap that is the topic of this dissertation. More specifically, we
propose (1) methods to formulate an effective query from complex textual
structures and (2) latent vector space models that circumvent the vocabulary
gap in information retrieval.
On evolutionary selection of blackjack strategies
Authors: Mikhail Goykhman
Date: 2017-11-16 00:00:00

We apply the approach of evolutionary programming to the problem of
optimization of the blackjack basic strategy. We demonstrate that the
population of initially random blackjack strategies evolves and saturates to a
profitable performance in about one hundred generations. The resulting strategy
resembles the known blackjack basic strategies in the specifics of its
prescriptions, and has a similar performance. We also study evolution of the
population of strategies initialized to the Thorp's basic strategy.
Related family-based attribute reduction of covering information systems when varying attribute sets
Authors: Guangming Lang
Date: 2017-11-16 00:00:00
Tasks: Attribute

In practical situations, there are many dynamic covering information systems
with variations of attributes, but there are few studies on related
family-based attribute reduction of dynamic covering information systems. In
this paper, we first investigate updated mechanisms of constructing attribute
reducts for consistent and inconsistent covering information systems when
varying attribute sets by using related families. Then we employ examples to
illustrate how to compute attribute reducts of dynamic covering information
systems with variations of attribute sets. Finally, the experimental results
illustrates that the related family-based methods are effective to perform
attribute reduction of dynamic covering information systems when attribute sets
are varying with time.
Coupled Deep Learning for Heterogeneous Face Recognition
Authors: Xiang Wu, Lingxiao Song, Ran He, Tieniu Tan
Date: 2017-04-08 00:00:00
Tasks: Deep Learning, Face Recognition, Heterogeneous Face Recognition, Triplet

Heterogeneous face matching is a challenge issue in face recognition due to
large domain difference as well as insufficient pairwise images in different
modalities during training. This paper proposes a coupled deep learning (CDL)
approach for the heterogeneous face matching. CDL seeks a shared feature space
in which the heterogeneous face matching problem can be approximately treated
as a homogeneous face matching problem. The objective function of CDL mainly
includes two parts. The first part contains a trace norm and a block-diagonal
prior as relevance constraints, which not only make unpaired images from
multiple modalities be clustered and correlated, but also regularize the
parameters to alleviate overfitting. An approximate variational formulation is
introduced to deal with the difficulties of optimizing low-rank constraint
directly. The second part contains a cross modal ranking among triplet domain
specific images to maximize the margin for different identities and increase
data for a small amount of training samples. Besides, an alternating
minimization method is employed to iteratively update the parameters of CDL.
Experimental results show that CDL achieves better performance on the
challenging CASIA NIR-VIS 2.0 face recognition database, the IIIT-D Sketch
database, the CUHK Face Sketch (CUFS), and the CUHK Face Sketch FERET (CUFSF),
which significantly outperforms state-of-the-art heterogeneous face recognition
methods.
Graph-Cut RANSAC
Authors: Daniel Barath, Jiri Matas
Date: 2017-06-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: CPU

A novel method for robust estimation, called Graph-Cut RANSAC, GC-RANSAC in
short, is introduced. To separate inliers and outliers, it runs the graph-cut
algorithm in the local optimization (LO) step which is applied when a
so-far-the-best model is found. The proposed LO step is conceptually simple,
easy to implement, globally optimal and efficient. GC-RANSAC is shown
experimentally, both on synthesized tests and real image pairs, to be more
geometrically accurate than state-of-the-art methods on a range of problems,
e.g. line fitting, homography, affine transformation, fundamental and essential
matrix estimation. It runs in real-time for many problems at a speed
approximately equal to that of the less accurate alternatives (in milliseconds
on standard CPU).
Multi-Class Model Fitting by Energy Minimization and Mode-Seeking
Authors: Daniel Barath, Jiri Matas
Date: 2017-06-02 00:00:00
Proceeding: ECCV 2018 9
Tasks: Motion Detection, Motion Segmentation

We propose a general formulation, called Multi-X, for multi-class
multi-instance model fitting - the problem of interpreting the input data as a
mixture of noisy observations originating from multiple instances of multiple
classes. We extend the commonly used alpha-expansion-based technique with a new
move in the label space. The move replaces a set of labels with the
corresponding density mode in the model parameter domain, thus achieving fast
and robust optimization. Key optimization parameters like the bandwidth of the
mode seeking are set automatically within the algorithm. Considering that a
group of outliers may form spatially coherent structures in the data, we
propose a cross-validation-based technique removing statistically insignificant
instances. Multi-X outperforms significantly the state-of-the-art on publicly
available datasets for diverse problems: multiple plane and rigid motion
detection; motion segmentation; simultaneous plane and cylinder fitting; circle
and line fitting.
Less-forgetful Learning for Domain Expansion in Deep Neural Networks
Authors: Heechul Jung, Jeongwoo Ju, Minju Jung, Junmo Kim
Date: 2017-11-16 00:00:00
Tasks: Domain Adaptation, image-classification, Image Classification

Expanding the domain that deep neural network has already learned without
accessing old domain data is a challenging task because deep neural networks
forget previously learned information when learning new data from a new domain.
In this paper, we propose a less-forgetful learning method for the domain
expansion scenario. While existing domain adaptation techniques solely focused
on adapting to new domains, the proposed technique focuses on working well with
both old and new domains without needing to know whether the input is from the
old or new domain. First, we present two naive approaches which will be
problematic, then we provide a new method using two proposed properties for
less-forgetful learning. Finally, we prove the effectiveness of our method
through experiments on image classification tasks. All datasets used in the
paper, will be released on our website for someone's follow-up study.
HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation
Authors: Qianqian Xu, Jiechao Xiong, Xi Chen, Qingming Huang, Yuan YAO
Date: 2017-11-16 00:00:00

Recently, crowdsourcing has emerged as an effective paradigm for
human-powered large scale problem solving in various domains. However, task
requester usually has a limited amount of budget, thus it is desirable to have
a policy to wisely allocate the budget to achieve better quality. In this
paper, we study the principle of information maximization for active sampling
strategies in the framework of HodgeRank, an approach based on Hodge
Decomposition of pairwise ranking data with multiple workers. The principle
exhibits two scenarios of active sampling: Fisher information maximization that
leads to unsupervised sampling based on a sequential maximization of graph
algebraic connectivity without considering labels; and Bayesian information
maximization that selects samples with the largest information gain from prior
to posterior, which gives a supervised sampling involving the labels collected.
Experiments show that the proposed methods boost the sampling efficiency as
compared to traditional sampling schemes and are thus valuable to practical
crowdsourcing experiments.
Zero-Annotation Object Detection with Web Knowledge Transfer
Authors: Qingyi Tao, Hao Yang, Jianfei Cai
Date: 2017-11-16 00:00:00
Proceeding: ECCV 2018 9
Tasks: Domain Adaptation, Object, object-detection, Object Detection, Transfer Learning

Object detection is one of the major problems in computer vision, and has
been extensively studied. Most of the existing detection works rely on
labor-intensive supervision, such as ground truth bounding boxes of objects or
at least image-level annotations. On the contrary, we propose an object
detection method that does not require any form of human annotation on target
tasks, by exploiting freely available web images. In order to facilitate
effective knowledge transfer from web images, we introduce a multi-instance
multi-label domain adaption learning framework with two key innovations. First
of all, we propose an instance-level adversarial domain adaptation network with
attention on foreground objects to transfer the object appearances from web
domain to target domain. Second, to preserve the class-specific semantic
structure of transferred object features, we propose a simultaneous transfer
mechanism to transfer the supervision across domains through pseudo strong
label generation. With our end-to-end framework that simultaneously learns a
weakly supervised detector and transfers knowledge across domains, we achieved
significant improvements over baseline methods on the benchmark datasets.
Enhanced Attacks on Defensively Distilled Deep Neural Networks
Authors: Yujia Liu, Weiming Zhang, Shaohua Li, Nenghai Yu
Date: 2017-11-16 00:00:00
Tasks: Face Recognition, General Classification, image-classification, Image Classification, Malware Detection

Deep neural networks (DNNs) have achieved tremendous success in many tasks of
machine learning, such as the image classification. Unfortunately, researchers
have shown that DNNs are easily attacked by adversarial examples, slightly
perturbed images which can mislead DNNs to give incorrect classification
results. Such attack has seriously hampered the deployment of DNN systems in
areas where security or safety requirements are strict, such as autonomous
cars, face recognition, malware detection. Defensive distillation is a
mechanism aimed at training a robust DNN which significantly reduces the
effectiveness of adversarial examples generation. However, the state-of-the-art
attack can be successful on distilled networks with 100% probability. But it is
a white-box attack which needs to know the inner information of DNN. Whereas,
the black-box scenario is more general. In this paper, we first propose the
epsilon-neighborhood attack, which can fool the defensively distilled networks
with 100% success rate in the white-box setting, and it is fast to generate
adversarial examples with good visual quality. On the basis of this attack, we
further propose the region-based attack against defensively distilled DNNs in
the black-box setting. And we also perform the bypass attack to indirectly
break the distillation defense as a complementary method. The experimental
results show that our black-box attacks have a considerable success rate on
defensively distilled networks.
Z-Forcing: Training Stochastic Recurrent Networks
Authors: Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio
Date: 2017-11-15 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Language Modeling, Language Modelling, Variational Inference

Many efforts have been devoted to training generative latent variable models
with autoregressive decoders, such as recurrent neural networks (RNN).
Stochastic recurrent models have been successful in capturing the variability
observed in natural sequential data such as speech. We unify successful ideas
from recently proposed architectures into a stochastic recurrent model: each
step in the sequence is associated with a latent variable that is used to
condition the recurrent dynamics for future steps. Training is performed with
amortized variational inference where the approximate posterior is augmented
with a RNN that runs backward through the sequence. In addition to maximizing
the variational lower bound, we ease training of the latent variables by adding
an auxiliary cost which forces them to reconstruct the state of the backward
recurrent network. This provides the latent variables with a task-independent
objective that enhances the performance of the overall model. We found this
strategy to perform better than alternative approaches such as KL annealing.
Although being conceptually simple, our model achieves state-of-the-art results
on standard speech benchmarks such as TIMIT and Blizzard and competitive
performance on sequential MNIST. Finally, we apply our model to language
modeling on the IMDB dataset where the auxiliary cost helps in learning
interpretable latent variables. Source Code:
\url{https://github.com/anirudh9119/zforcing_nips17}
Budget-Constrained Multi-Armed Bandits with Multiple Plays
Authors: Datong P. Zhou, Claire J. Tomlin
Date: 2017-11-16 00:00:00
Tasks: Multi-Armed Bandits

We study the multi-armed bandit problem with multiple plays and a budget
constraint for both the stochastic and the adversarial setting. At each round,
exactly $K$ out of $N$ possible arms have to be played (with $1\leq K \leq N$).
In addition to observing the individual rewards for each arm played, the player
also learns a vector of costs which has to be covered with an a-priori defined
budget $B$. The game ends when the sum of current costs associated with the
played arms exceeds the remaining budget.
  Firstly, we analyze this setting for the stochastic case, for which we assume
each arm to have an underlying cost and reward distribution with support
$[c_{\min}, 1]$ and $[0, 1]$, respectively. We derive an Upper Confidence Bound
(UCB) algorithm which achieves $O(NK^4 \log B)$ regret.
  Secondly, for the adversarial case in which the entire sequence of rewards
and costs is fixed in advance, we derive an upper bound on the regret of order
$O(\sqrt{NB\log(N/K)})$ utilizing an extension of the well-known
$\texttt{Exp3}$ algorithm. We also provide upper bounds that hold with high
probability and a lower bound of order $\Omega((1 - K/N)^2 \sqrt{NB/K})$.
Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction
Authors: Chamara Saroj Weerasekera, Ravi Garg, Yasir Latif, Ian Reid
Date: 2017-11-16 00:00:00
Tasks: Depth Estimation, Monocular Reconstruction, Simultaneous Localization and Mapping

Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on
handcrafted visual features or raw RGB values for establishing correspondences
between images. These features, while suitable for sparse mapping, often lead
to ambiguous matches in texture-less regions when performing dense
reconstruction due to the aperture problem. In this work, we explore the use of
learned features for the matching task in dense monocular reconstruction. We
propose a novel convolutional neural network (CNN) architecture along with a
deeply supervised feature learning scheme for pixel-wise regression of visual
descriptors from an image which are best suited for dense monocular SLAM. In
particular, our learning scheme minimizes a multi-view matching cost-volume
loss with respect to the regressed features at multiple stages within the
network, for explicitly learning contextual features that are suitable for
dense matching between images captured by a moving monocular camera along the
epipolar line. We integrate the learned features from our model for depth
estimation inside a real-time dense monocular SLAM framework, where photometric
error is replaced by our learned descriptor error. Our extensive evaluation on
several challenging indoor datasets demonstrate greatly improved accuracy in
dense reconstructions of the well celebrated dense SLAM systems like DTAM,
without compromising their real-time performance.
Using experimental game theory to transit human values to ethical AI
Authors: Yijia Wang, Yan Wan, Zhijian Wang
Date: 2017-11-16 00:00:00
Tasks: Ethics, Philosophy

Knowing the reflection of game theory and ethics, we develop a mathematical
representation to bridge the gap between the concepts in moral philosophy
(e.g., Kantian and Utilitarian) and AI ethics industry technology standard
(e.g., IEEE P7000 standard series for Ethical AI). As an application, we
demonstrate how human value can be obtained from the experimental game theory
(e.g., trust game experiment) so as to build an ethical AI. Moreover, an
approach to test the ethics (rightness or wrongness) of a given AI algorithm by
using an iterated Prisoner's Dilemma Game experiment is discussed as an
example. Compared with existing mathematical frameworks and testing method on
AI ethics technology, the advantages of the proposed approach are analyzed.
Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification
Authors: Bo-Ru Lu, Frank Shyu, Yun-Nung Chen, Hung-Yi Lee, Lin-shan Lee
Date: 2017-09-16 00:00:00
Tasks: Abstractive Text Summarization, General Classification, speech-recognition, Speech Recognition

Connectionist temporal classification (CTC) is a powerful approach for
sequence-to-sequence learning, and has been popularly used in speech
recognition. The central ideas of CTC include adding a label "blank" during
training. With this mechanism, CTC eliminates the need of segment alignment,
and hence has been applied to various sequence-to-sequence learning problems.
In this work, we applied CTC to abstractive summarization for spoken content.
The "blank" in this case implies the corresponding input data are less
important or noisy; thus it can be ignored. This approach was shown to
outperform the existing methods in term of ROUGE scores over Chinese Gigaword
and MATBN corpora. This approach also has the nice property that the ordering
of words or characters in the input documents can be better preserved in the
generated summaries.
Neural Natural Language Inference Models Enhanced with External Knowledge
Authors: Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen, Si Wei
Date: 2017-11-12 00:00:00
Proceeding: ACL 2018 7
Tasks: Natural Language Inference

Modeling natural language inference is a very challenging task. With the
availability of large annotated data, it has recently become feasible to train
complex models such as neural-network-based inference models, which have shown
to achieve the state-of-the-art performance. Although there exist relatively
large annotated data, can machines learn all knowledge needed to perform
natural language inference (NLI) from these data? If not, how can
neural-network-based NLI models benefit from external knowledge and how to
build NLI models to leverage it? In this paper, we enrich the state-of-the-art
neural natural language inference models with external knowledge. We
demonstrate that the proposed models improve neural NLI models to achieve the
state-of-the-art performance on the SNLI and MultiNLI datasets.