pwc-archive/papers-with-abstracts huggingface dataset:
Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation
Authors: Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan, Trung Pham, Huu Le, Ngai-Man Cheung, Ian Reid
Date: 2018-02-21 00:00:00
Tasks: Deep Hashing, Image Retrieval, Quantization, Retrieval

Learning compact binary codes for image retrieval task using deep neural
networks has attracted increasing attention recently. However, training deep
hashing networks for the task is challenging due to the binary constraints on
the hash codes, the similarity preserving property, and the requirement for a
vast amount of labelled images. To the best of our knowledge, none of the
existing methods has tackled all of these challenges completely in a unified
framework. In this work, we propose a novel end-to-end deep learning approach
for the task, in which the network is trained to produce binary codes directly
from image pixels without the need of manual annotation. In particular, to deal
with the non-smoothness of binary constraints, we propose a novel pairwise
constrained loss function, which simultaneously encodes the distances between
pairs of hash codes, and the binary quantization error. In order to train the
network with the proposed loss function, we propose an efficient parameter
learning algorithm. In addition, to provide similar / dissimilar training
images to train the network, we exploit 3D models reconstructed from unlabelled
images for automatic generation of enormous training image pairs. The extensive
experiments on image retrieval benchmark datasets demonstrate the improvements
of the proposed method over the state-of-the-art compact representation methods
on the image retrieval problem.
Text-Independent Speaker Verification Using Long Short-Term Memory Networks
Authors: Aryan Mobiny, Mohammad Najarian
Date: 2018-05-02 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

In this paper, an architecture based on Long Short-Term Memory Networks has
been proposed for the text-independent scenario which is aimed to capture the
temporal speaker-related information by operating over traditional speech
features. For speaker verification, at first, a background model must be
created for speaker representation. Then, in enrollment stage, the speaker
models will be created based on the enrollment utterances. For this work, the
model will be trained in an end-to-end fashion to combine the first two stages.
The main goal of end-to-end training is the model being optimized to be
consistent with the speaker verification protocol. The end- to-end training
jointly learns the background and speaker models by creating the representation
space. The LSTM architecture is trained to create a discrimination space for
validating the match and non-match pairs for speaker verification. The proposed
architecture demonstrate its superiority in the text-independent compared to
other traditional methods.
Bi-directional Graph Structure Information Model for Multi-Person Pose Estimation
Authors: Jing Wang, Ze Peng, Pei Lv, Junyi Sun, Bing Zhou, Mingliang Xu
Date: 2018-05-02 00:00:00
Tasks: Multi-Person Pose Estimation, Pose Estimation

In this paper, we propose a novel multi-stage network architecture with two
branches in each stage to estimate multi-person poses in images. The first
branch predicts the confidence maps of joints and uses a geometrical transform
kernel to propagate information between neighboring joints at the confidence
level. The second branch proposes a bi-directional graph structure information
model (BGSIM) to encode rich contextual information and to infer the occlusion
relationship among different joints. We dynamically determine the joint point
with highest response of the confidence maps as base point of passing message
in BGSIM. Based on the proposed network structure, we achieve an average
precision of 62.9 on the COCO Keypoint Challenge dataset and 77.6 on the MPII
(multi-person) dataset. Compared with other state-of-art methods, our method
can achieve highly promising results on our selected multi-person dataset
without extra training.
Evaluation of Feature Detector-Descriptor for Real Object Matching under Various Conditions of Ilumination and Affine Transformation
Authors: Novanto Yudistira, Achmad Ridok, Ali Fauzi
Date: 2018-04-28 00:00:00

This study attempts to provide explanations, descriptions and evaluations of
some most popular and current combinations of description and descriptor
frameworks, namely SIFT, SURF, MSER, and BRISK for keypoint extractors and
SIFT, SURF, BRISK, and FREAK for descriptors. Evaluations are made based on the
number of matches of keypoints and repeatability in various image variations.
It is used as the main parameter to assess how well combinations of algorithms
are in matching objects with different variations. There are many papers that
describe the comparison of detection and description features to detect objects
in images under various conditions, but the combination of algorithms attached
to them has not been much discussed. The problem domain is limited to different
illumination levels and affine transformations from different perspectives. To
evaluate the robustness of all combinations of algorithms, we use a stereo
image matching case.
Structured Analysis Dictionary Learning for Image Classification
Authors: Wen Tang, Ashkan Panahi, Hamid Krim, Liyi Dai
Date: 2018-05-02 00:00:00
Tasks: Classification, Dictionary Learning, General Classification, image-classification, Image Classification

We propose a computationally efficient and high-performance classification
algorithm by incorporating class structural information in analysis dictionary
learning. To achieve more consistent classification, we associate a class
characteristic structure of independent subspaces and impose it on the
classification error constrained analysis dictionary learning. Experiments
demonstrate that our method achieves a comparable or better performance than
the state-of-the-art algorithms in a variety of visual classification tasks. In
addition, our method greatly reduces the training and testing computational
complexity.
How morphological development can guide evolution
Authors: Sam Kriegman, Nick Cheney, Josh Bongard
Date: 2017-11-20 00:00:00

Organisms result from adaptive processes interacting across different time
scales. One such interaction is that between development and evolution. Models
have shown that development sweeps over several traits in a single agent,
sometimes exposing promising static traits. Subsequent evolution can then
canalize these rare traits. Thus, development can, under the right conditions,
increase evolvability. Here, we report on a previously unknown phenomenon when
embodied agents are allowed to develop and evolve: Evolution discovers body
plans robust to control changes, these body plans become genetically
assimilated, yet controllers for these agents are not assimilated. This allows
evolution to continue climbing fitness gradients by tinkering with the
developmental programs for controllers within these permissive body plans. This
exposes a previously unknown detail about the Baldwin effect: instead of all
useful traits becoming genetically assimilated, only traits that render the
agent robust to changes in other traits become assimilated. We refer to this as
differential canalization. This finding also has implications for the
evolutionary design of artificial and embodied agents such as robots: robots
robust to internal changes in their controllers may also be robust to external
changes in their environment, such as transferal from simulation to reality or
deployment in novel environments.
Perspectival Knowledge in PSOA RuleML: Representation, Model Theory, and Translation
Authors: Harold Boley, Gen Zou
Date: 2017-12-07 00:00:00
Tasks: Attribute, Translation

In Positional-Slotted Object-Applicative (PSOA) RuleML, a predicate application (atom) can have an Object IDentifier (OID) and descriptors that may be positional arguments (tuples) or attribute-value pairs (slots). PSOA RuleML explicitly specifies for each descriptor whether it is to be interpreted under the perspective of the predicate in whose scope it occurs. This predicate-dependency dimension refines the space between oidless, positional atoms (relationships) and oidful, slotted atoms (framepoints): While relationships use only a predicate-scope-sensitive (predicate-dependent) tuple and framepoints use only predicate-scope-insensitive (predicate-independent) slots, PSOA uses a systematics of orthogonal constructs also permitting atoms with (predicate-)independent tuples and atoms with (predicate-)dependent slots. This supports data and knowledge representation where a slot attribute can have different values depending on the predicate. PSOA thus extends object-oriented multi-membership and multiple inheritance. Based on objectification, PSOA laws are given: Besides unscoping and centralization, the semantic restriction and transformation of describution permits rescoping of one atom's independent descriptors to another atom with the same OID but a different predicate. For inheritance, default descriptors are realized by rules. On top of a metamodel and a Grailog visualization, PSOA's atom systematics for facts, queries, and rules is explained. The presentation and (XML-)serialization syntaxes of PSOA RuleML are introduced. Its model-theoretic semantics is formalized by extending the interpretation functions for dependent descriptors. The open-source PSOATransRun system realizes PSOA RuleML by a translator to runtime predicates, including for dependent tuples (prdtupterm) and slots (prdsloterm). Our tests show efficiency advantages of dependent and tupled modeling.
Fused Deep Neural Networks for Efficient Pedestrian Detection
Authors: Xianzhi Du, Mostafa El-Khamy, Vlad I. Morariu, Jungwon Lee, Larry Davis
Date: 2018-05-02 00:00:00
Tasks: Ensemble Learning, General Classification, Pedestrian Detection, Semantic Segmentation

In this paper, we present an efficient pedestrian detection system, designed
by fusion of multiple deep neural network (DNN) systems. Pedestrian candidates
are first generated by a single shot convolutional multi-box detector at
different locations with various scales and aspect ratios. The candidate
generator is designed to provide the majority of ground truth pedestrian
annotations at the cost of a large number of false positives. Then, a
classification system using the idea of ensemble learning is deployed to
improve the detection accuracy. The classification system further classifies
the generated candidates based on opinions of multiple deep verification
networks and a fusion network which utilizes a novel soft-rejection fusion
method to adjust the confidence in the detection results. To improve the
training of the deep verification networks, a novel soft-label method is
devised to assign floating point labels to the generated pedestrian candidates.
A deep context aggregation semantic segmentation network also provides
pixel-level classification of the scene and its results are softly fused with
the detection results by the single shot detector. Our pedestrian detector
compared favorably to state-of-art methods on all popular pedestrian detection
datasets. For example, our fused DNN has better detection accuracy on the
Caltech Pedestrian dataset than all previous state of art methods, while also
being the fastest. We significantly improved the log-average miss rate on the
Caltech pedestrian dataset to 7.67% and achieved the new state-of-the-art.
Convolutional-Recurrent Neural Networks for Speech Enhancement
Authors: Han Zhao, Shuayb Zarar, Ivan Tashev, Chin-Hui Lee
Date: 2018-05-02 00:00:00
Tasks: Speech Enhancement

We propose an end-to-end model based on convolutional and recurrent neural
networks for speech enhancement. Our model is purely data-driven and does not
make any assumptions about the type or the stationarity of the noise. In
contrast to existing methods that use multilayer perceptrons (MLPs), we employ
both convolutional and recurrent neural network architectures. Thus, our
approach allows us to exploit local structures in both the frequency and
temporal domains. By incorporating prior knowledge of speech signals into the
design of model structures, we build a model that is more data-efficient and
achieves better generalization on both seen and unseen noise. Based on
experiments with synthetic data, we demonstrate that our model outperforms
existing methods, improving PESQ by up to 0.6 on seen noise and 0.64 on unseen
noise.
Secure Face Matching Using Fully Homomorphic Encryption
Authors: Vishnu Naresh Boddeti
Date: 2018-05-01 00:00:00
Tasks: Dimensionality Reduction, Face Recognition, Representation Learning, Template Matching

Face recognition technology has demonstrated tremendous progress over the
past few years, primarily due to advances in representation learning. As we
witness the widespread adoption of these systems, it is imperative to consider
the security of face representations. In this paper, we explore the
practicality of using a fully homomorphic encryption based framework to secure
a database of face templates. This framework is designed to preserve the
privacy of users and prevent information leakage from the templates, while
maintaining their utility through template matching directly in the encrypted
domain. Additionally, we also explore a batching and dimensionality reduction
scheme to trade-off face matching accuracy and computational complexity.
Experiments on benchmark face datasets (LFW, IJB-A, IJB-B, CASIA) indicate that
secure face matching can be practically feasible (16 KB template size and 0.01
sec per match pair for 512-dimensional features from SphereFace) while
exhibiting minimal loss in matching performance.
Controllable Generative Adversarial Network
Authors: Minhyeok Lee, Junhee Seok
Date: 2017-08-02 00:00:00
Tasks: Generative Adversarial Network

Recently introduced generative adversarial network (GAN) has been shown
numerous promising results to generate realistic samples. The essential task of
GAN is to control the features of samples generated from a random distribution.
While the current GAN structures, such as conditional GAN, successfully
generate samples with desired major features, they often fail to produce
detailed features that bring specific differences among samples. To overcome
this limitation, here we propose a controllable GAN (ControlGAN) structure. By
separating a feature classifier from a discriminator, the generator of
ControlGAN is designed to learn generating synthetic samples with the specific
detailed features. Evaluated with multiple image datasets, ControlGAN shows a
power to generate improved samples with well-controlled features. Furthermore,
we demonstrate that ControlGAN can generate intermediate features and opposite
features for interpolated and extrapolated input labels that are not used in
the training process. It implies that ControlGAN can significantly contribute
to the variety of generated samples.
Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties
Authors: Michael Eickenberg, Georgios Exarchakis, Matthew Hirn, Stéphane Mallat, Louis Thiry
Date: 2018-05-01 00:00:00
Tasks: BIG-bench Machine Learning

We present a machine learning algorithm for the prediction of molecule
properties inspired by ideas from density functional theory. Using
Gaussian-type orbital functions, we create surrogate electronic densities of
the molecule from which we compute invariant "solid harmonic scattering
coefficients" that account for different types of interactions at different
scales. Multi-linear regressions of various physical properties of molecules
are computed from these invariant coefficients. Numerical experiments show that
these regressions have near state of the art performance, even with relatively
few training examples. Predictions over small sets of scattering coefficients
can reach a DFT precision while being interpretable.
Decision Tree Design for Classification in Crowdsourcing Systems
Authors: Baocheng Geng, Qunwei Li, Pramod K. Varshney
Date: 2018-05-01 00:00:00
Tasks: Classification, General Classification

In this paper, we present a novel sequential paradigm for classification in
crowdsourcing systems. Considering that workers are unreliable and they perform
the tests with errors, we study the construction of decision trees so as to
minimize the probability of mis-classification. By exploiting the connection
between probability of mis-classification and entropy at each level of the
decision tree, we propose two algorithms for decision tree design. Furthermore,
the worker assignment problem is studied when workers can be assigned to
different tests of the decision tree to provide a trade-off between
classification cost and resulting error performance. Numerical results are
presented for illustration.
Exploring Conversational Language Generation for Rich Content about Hotels
Authors: Marilyn A. Walker, Albry Smither, Shereen Oraby, Vrindavan Harrison, Hadar Shemtov
Date: 2018-05-01 00:00:00
Proceeding: LREC 2018 5
Tasks: Text Generation

Dialogue systems for hotel and tourist information have typically simplified
the richness of the domain, focusing system utterances on only a few selected
attributes such as price, location and type of rooms. However, much more
content is typically available for hotels, often as many as 50 distinct
instantiated attributes for an individual entity. New methods are needed to use
this content to generate natural dialogues for hotel information, and in
general for any domain with such rich complex content. We describe three
experiments aimed at collecting data that can inform an NLG for hotels
dialogues, and show, not surprisingly, that the sentences in the original
written hotel descriptions provided on webpages for each hotel are
stylistically not a very good match for conversational interaction. We quantify
the stylistic features that characterize the differences between the original
textual data and the collected dialogic data. We plan to use these in stylistic
models for generation, and for scoring retrieved utterances for use in hotel
dialogues
Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning
Authors: Ronald Kemker, Carl Salvaggio, Christopher Kanan
Date: 2017-03-19 00:00:00
Tasks: object-detection, Object Detection, Object Recognition, Segmentation, Semantic Segmentation

Deep convolutional neural networks (DCNNs) have been used to achieve
state-of-the-art performance on many computer vision tasks (e.g., object
recognition, object detection, semantic segmentation) thanks to a large
repository of annotated image data. Large labeled datasets for other sensor
modalities, e.g., multispectral imagery (MSI), are not available due to the
large cost and manpower required. In this paper, we adapt state-of-the-art DCNN
frameworks in computer vision for semantic segmentation for MSI imagery. To
overcome label scarcity for MSI data, we substitute real MSI for generated
synthetic MSI in order to initialize a DCNN framework. We evaluate our network
initialization scheme on the new RIT-18 dataset that we present in this paper.
This dataset contains very-high resolution MSI collected by an unmanned
aircraft system. The models initialized with synthetic imagery were less prone
to over-fitting and provide a state-of-the-art baseline for future work.
Weakly Supervised Attention Learning for Textual Phrases Grounding
Authors: Zhiyuan Fang, Shu Kong, Tianshu Yu, Yezhou Yang
Date: 2018-05-01 00:00:00

Grounding textual phrases in visual content is a meaningful yet challenging
problem with various potential applications such as image-text inference or
text-driven multimedia interaction. Most of the current existing methods adopt
the supervised learning mechanism which requires ground-truth at pixel level
during training. However, fine-grained level ground-truth annotation is quite
time-consuming and severely narrows the scope for more general applications. In
this extended abstract, we explore methods to localize flexibly image regions
from the top-down signal (in a form of one-hot label or natural languages) with
a weakly supervised attention learning mechanism. In our model, two types of
modules are utilized: a backbone module for visual feature capturing, and an
attentive module generating maps based on regularized bilinear pooling. We
construct the model in an end-to-end fashion which is trained by encouraging
the spatial attentive map to shift and focus on the region that consists of the
best matched visual features with the top-down signal. We demonstrate the
preliminary yet promising results on a testbed that is synthesized with
multi-label MNIST data.
ES Is More Than Just a Traditional Finite-Difference Approximator
Authors: Joel Lehman, Jay Chen, Jeff Clune, Kenneth O. Stanley
Date: 2017-12-18 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

An evolution strategy (ES) variant based on a simplification of a natural
evolution strategy recently attracted attention because it performs
surprisingly well in challenging deep reinforcement learning domains. It
searches for neural network parameters by generating perturbations to the
current set of parameters, checking their performance, and moving in the
aggregate direction of higher reward. Because it resembles a traditional
finite-difference approximation of the reward gradient, it can naturally be
confused with one. However, this ES optimizes for a different gradient than
just reward: It optimizes for the average reward of the entire population,
thereby seeking parameters that are robust to perturbation. This difference can
channel ES into distinct areas of the search space relative to gradient
descent, and also consequently to networks with distinct properties. This
unique robustness-seeking property, and its consequences for optimization, are
demonstrated in several domains. They include humanoid locomotion, where
networks from policy gradient-based reinforcement learning are significantly
less robust to parameter perturbation than ES-based policies solving the same
task. While the implications of such robustness and robustness-seeking remain
open to further study, this work's main contribution is to highlight such
differences and their potential importance.
Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients
Authors: Joel Lehman, Jay Chen, Jeff Clune, Kenneth O. Stanley
Date: 2017-12-18 00:00:00
Tasks: Artificial Life, Reinforcement Learning

While neuroevolution (evolving neural networks) has a successful track record
across a variety of domains from reinforcement learning to artificial life, it
is rarely applied to large, deep neural networks. A central reason is that
while random mutation generally works in low dimensions, a random perturbation
of thousands or millions of weights is likely to break existing functionality,
providing no learning signal even if some individual weight changes were
beneficial. This paper proposes a solution by introducing a family of safe
mutation (SM) operators that aim within the mutation operator itself to find a
degree of change that does not alter network behavior too much, but still
facilitates exploration. Importantly, these SM operators do not require any
additional interactions with the environment. The most effective SM variant
capitalizes on the intriguing opportunity to scale the degree of mutation of
each individual weight according to the sensitivity of the network's outputs to
that weight, which requires computing the gradient of outputs with respect to
the weights (instead of the gradient of error, as in conventional deep
learning). This safe mutation through gradients (SM-G) operator dramatically
increases the ability of a simple genetic algorithm-based neuroevolution method
to find solutions in high-dimensional domains that require deep and/or
recurrent neural networks (which tend to be particularly brittle to mutation),
including domains that require processing raw pixels. By improving our ability
to evolve deep neural networks, this new safer approach to mutation expands the
scope of domains amenable to neuroevolution.