pwc-archive/papers-with-abstracts huggingface dataset:
Creativity and Artificial Intelligence: A Digital Art Perspective
Authors: Bo Xing, Tshilidzi Marwala
Date: 2018-07-21 00:00:00
Tasks: BIG-bench Machine Learning

This paper describes the application of artificial intelligence to the
creation of digital art. AI is a computational paradigm that codifies
intelligence into machines. There are generally three types of artificial
intelligence and these are machine learning, evolutionary programming and soft
computing. Machine learning is the statistical approach to building intelligent
systems. Evolutionary programming is the use of natural evolutionary systems to
design intelligent machines. Some of the evolutionary programming systems
include genetic algorithm which is inspired by the principles of evolution and
swarm optimization which is inspired by the swarming of birds, fish, ants etc.
Soft computing includes techniques such as agent based modelling and fuzzy
logic. Opportunities on the applications of these to digital art are explored.
Towards Distributed Coevolutionary GANs
Authors: Abdullah Al-Dujaili, Tom Schmiedlechner, and Erik Hemberg, Una-May O'Reilly
Date: 2018-07-21 00:00:00

Generative Adversarial Networks (GANs) have become one of the dominant
methods for deep generative modeling. Despite their demonstrated success on
multiple vision tasks, GANs are difficult to train and much research has been
dedicated towards understanding and improving their gradient-based learning
dynamics. Here, we investigate the use of coevolution, a class of black-box
(gradient-free) co-optimization techniques and a powerful tool in evolutionary
computing, as a supplement to gradient-based GAN training techniques.
Experiments on a simple model that exhibits several of the GAN gradient-based
dynamics (e.g., mode collapse, oscillatory behavior, and vanishing gradients)
show that coevolution is a promising framework for escaping degenerate GAN
training behaviors.
Decouple Learning for Parameterized Image Operators
Authors: Qingnan Fan, Dong-Dong Chen, Lu Yuan, Gang Hua, Nenghai Yu, Baoquan Chen
Date: 2018-07-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: Denoising, image smoothing, Super-Resolution

Many different deep networks have been used to approximate, accelerate or
improve traditional image operators, such as image smoothing, super-resolution
and denoising. Among these traditional operators, many contain parameters which
need to be tweaked to obtain the satisfactory results, which we refer to as
"parameterized image operators". However, most existing deep networks trained
for these operators are only designed for one specific parameter configuration,
which does not meet the needs of real scenarios that usually require flexible
parameters settings. To overcome this limitation, we propose a new decouple
learning algorithm to learn from the operator parameters to dynamically adjust
the weights of a deep network for image operators, denoted as the base network.
The learned algorithm is formed as another network, namely the weight learning
network, which can be end-to-end jointly trained with the base network.
Experiments demonstrate that the proposed framework can be successfully applied
to many traditional parameterized image operators. We provide more analysis to
better understand the proposed framework, which may inspire more promising
research in this direction. Our codes and models have been released in
https://github.com/fqnchina/DecoupleLearning
Inductive Visual Localisation: Factorised Training for Superior Generalisation
Authors: Ankush Gupta, Andrea Vedaldi, Andrew Zisserman
Date: 2018-07-21 00:00:00
Tasks: Image Captioning, Machine Translation, Text Spotting, valid

End-to-end trained Recurrent Neural Networks (RNNs) have been successfully
applied to numerous problems that require processing sequences, such as image
captioning, machine translation, and text recognition. However, RNNs often
struggle to generalise to sequences longer than the ones encountered during
training. In this work, we propose to optimise neural networks explicitly for
induction. The idea is to first decompose the problem in a sequence of
inductive steps and then to explicitly train the RNN to reproduce such steps.
Generalisation is achieved as the RNN is not allowed to learn an arbitrary
internal state; instead, it is tasked with mimicking the evolution of a valid
state. In particular, the state is restricted to a spatial memory map that
tracks parts of the input image which have been accounted for in previous
steps. The RNN is trained for single inductive steps, where it produces updates
to the memory in addition to the desired output. We evaluate our method on two
different visual recognition problems involving visual sequences: (1) text
spotting, i.e. joint localisation and reading of text in images containing
multiple lines (or a block) of text, and (2) sequential counting of objects in
aerial images. We show that inductive training of recurrent models enhances
their generalisation ability on challenging image datasets.
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
Authors: Alberto Rossi, Gianni Barlacchi, Monica Bianchini, Bruno Lepri
Date: 2018-07-21 00:00:00

In this paper, we study how to model taxi drivers' behaviour and geographical
information for an interesting and challenging task: the next destination
prediction in a taxi journey. Predicting the next location is a well studied
problem in human mobility, which finds several applications in real-world
scenarios, from optimizing the efficiency of electronic dispatching systems to
predicting and reducing the traffic jam. This task is normally modeled as a
multiclass classification problem, where the goal is to select, among a set of
already known locations, the next taxi destination. We present a Recurrent
Neural Network (RNN) approach that models the taxi drivers' behaviour and
encodes the semantics of visited locations by using geographical information
from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to
predict the exact coordinates of the next destination, overcoming the problem
of producing, in output, a limited set of locations, seen during the training
phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge
2015 dataset - based on the city of Porto -, obtaining better results with
respect to the competition winner, whilst using less information, and on
Manhattan and San Francisco datasets.
Recent Advances in Deep Learning: An Overview
Authors: Matiur Rahman Minar, Jibon Naher
Date: 2018-07-21 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Deep Learning is one of the newest trends in Machine Learning and Artificial
Intelligence research. It is also one of the most popular scientific research
trends now-a-days. Deep learning methods have brought revolutionary advances in
computer vision and machine learning. Every now and then, new and new deep
learning techniques are being born, outperforming state-of-the-art machine
learning and even existing deep learning techniques. In recent years, the world
has seen many major breakthroughs in this field. Since deep learning is
evolving at a huge speed, its kind of hard to keep track of the regular
advances especially for new researchers. In this paper, we are going to briefly
discuss about recent advances in Deep Learning for past few years.
Linear density-based clustering with a discrete density model
Authors: Roberto Pirrone, Vincenzo Cannella, Sergio Monteleone, Gabriella Giordano
Date: 2018-07-21 00:00:00
Tasks: Clustering

Density-based clustering techniques are used in a wide range of data mining
applications. One of their most attractive features con- sists in not making
use of prior knowledge of the number of clusters that a dataset contains along
with their shape. In this paper we propose a new algorithm named Linear DBSCAN
(Lin-DBSCAN), a simple approach to clustering inspired by the density model
introduced with the well known algorithm DBSCAN. Designed to minimize the
computational cost of density based clustering on geospatial data, Lin-DBSCAN
features a linear time complexity that makes it suitable for real-time
applications on low-resource devices. Lin-DBSCAN uses a discrete version of the
density model of DBSCAN that takes ad- vantage of a grid-based scan and merge
approach. The name of the algorithm stems exactly from its main features
outlined above. The algorithm was tested with well known data sets.
Experimental results prove the efficiency and the validity of this approach
over DBSCAN in the context of spatial data clustering, enabling the use of a
density-based clustering technique on large datasets with low computational
cost.
On the Analysis of Trajectories of Gradient Descent in the Optimization of Deep Neural Networks
Authors: Adepu Ravi Sankar, Vishwak Srinivasan, Vineeth N. Balasubramanian
Date: 2018-07-21 00:00:00

Theoretical analysis of the error landscape of deep neural networks has
garnered significant interest in recent years. In this work, we theoretically
study the importance of noise in the trajectories of gradient descent towards
optimal solutions in multi-layer neural networks. We show that adding noise (in
different ways) to a neural network while training increases the rank of the
product of weight matrices of a multi-layer linear neural network. We thus
study how adding noise can assist reaching a global optimum when the product
matrix is full-rank (under certain conditions). We establish theoretical
foundations between the noise induced into the neural network - either to the
gradient, to the architecture, or to the input/output to a neural network - and
the rank of product of weight matrices. We corroborate our theoretical findings
with empirical results.
Integrating Feature and Image Pyramid: A Lung Nodule Detector Learned in Curriculum Fashion
Authors: Benyuan Sun, Zhen Zhou, Fandong Zhang, Xiuli Li, Yizhou Wang
Date: 2018-07-21 00:00:00

Lung nodules suffer large variation in size and appearance in CT images.
Nodules less than 10mm can easily lose information after down-sampling in
convolutional neural networks, which results in low sensitivity. In this paper,
a combination of 3D image and feature pyramid is exploited to integrate
lower-level texture features with high-level semantic features, thus leading to
a higher recall. However, 3D operations are time and memory consuming, which
aggravates the situation with the explosive growth of medical images. To tackle
this problem, we propose a general curriculum training strategy to speed up
training. An dynamic sampling method is designed to pick up partial samples
which give the best contribution to network training, thus leading to much less
time consuming. In experiments, we demonstrate that the proposed network
outperforms previous state-of-the-art methods. Meanwhile, our sampling strategy
halves the training time of the proposal network on LUNA16.
What is not where: the challenge of integrating spatial representations into deep learning architectures
Authors: John D. Kelleher, Simon Dobnik
Date: 2018-07-21 00:00:00
Tasks: Caption Generation, Deep Learning, Image Captioning, Language Modeling, Language Modelling

This paper examines to what degree current deep learning architectures for
image caption generation capture spatial language. On the basis of the
evaluation of examples of generated captions from the literature we argue that
systems capture what objects are in the image data but not where these objects
are located: the captions generated by these systems are the output of a
language model conditioned on the output of an object detector that cannot
capture fine-grained location information. Although language models provide
useful knowledge for image captions, we argue that deep learning image
captioning architectures should also model geometric relations between objects.
Coupled dictionary learning for unsupervised change detection between multi-sensor remote sensing images
Authors: Vinicius Ferraris, Nicolas Dobigeon, Yanna Cavalcanti, Thomas Oberlin, Marie Chabert
Date: 2018-07-21 00:00:00
Tasks: Change Detection, Dictionary Learning

Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images available may be those acquired through sensors of different modalities. This paper addresses the problem of unsupervisedly detecting changes between two observed images acquired by sensors of different modalities with possibly different resolutions. These sensor dissimilarities introduce additional issues in the context of operational change detection that are not addressed by most of the classical methods. This paper introduces a novel framework to effectively exploit the available information by modelling the two observed images as a sparse linear combination of atoms belonging to a pair of coupled overcomplete dictionaries learnt from each observed image. As they cover the same geographical location, codes are expected to be globally similar, except for possible changes in sparse spatial locations. Thus, the change detection task is envisioned through a dual code estimation which enforces spatial sparsity in the difference between the estimated codes associated with each image. This problem is formulated as an inverse problem which is iteratively solved using an efficient proximal alternating minimization algorithm accounting for nonsmooth and nonconvex functions. The proposed method is applied to real images with simulated yet realistic and real changes. A comparison with state-of-the-art change detection methods evidences the accuracy of the proposed strategy.
Multiple Convolutional Neural Network for Skin Dermoscopic Image Classification
Authors: Yanhui Guo, Amira S. Ashour
Date: 2018-07-21 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Melanoma classification is a serious stage to identify the skin disease. It
is considered a challenging process due to the intra-class discrepancy of
melanomas, skin lesions low contrast, and the artifacts in the dermoscopy
images, including noise, existence of hair, air bubbles, and the similarity
between melanoma and non-melanoma cases. To solve these problems, we propose a
novel multiple convolution neural network model (MCNN) to classify different
seven disease types in dermoscopic images, where several models were trained
separately using an additive sample learning strategy. The MCNN model is
trained and tested using the training and validation sets from the
International Skin Imaging Collaboration (ISIC 2018), respectively. The
receiver operating characteristic (ROC) curve is used to evaluate the
performance of the proposed method. The values of AUC (the area under the ROC
curve) were used to evaluate the performance of the MCNN.
Simultaneous Adversarial Training - Learn from Others Mistakes
Authors: Zukang Liao
Date: 2018-07-21 00:00:00
Tasks: Domain Adaptation

Adversarial examples are maliciously tweaked images that can easily fool
machine learning techniques, such as neural networks, but they are normally not
visually distinguishable for human beings. One of the main approaches to solve
this problem is to retrain the networks using those adversarial examples,
namely adversarial training. However, standard adversarial training might not
actually change the decision boundaries but cause the problem of gradient
masking, resulting in a weaker ability to generate adversarial examples.
Therefore, it cannot alleviate the problem of black-box attacks, where
adversarial examples generated from other networks can transfer to the targeted
one. In order to reduce the problem of black-box attacks, we propose a novel
method that allows two networks to learn from each others' adversarial examples
and become resilient to black-box attacks. We also combine this method with a
simple domain adaptation to further improve the performance.
Person Search via A Mask-Guided Two-Stream CNN Model
Authors: Di Chen, Shanshan Zhang, Wanli Ouyang, Jian Yang, Ying Tai
Date: 2018-07-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: Pedestrian Detection, Person Re-Identification, Person Search, Vocal Bursts Valence Prediction

In this work, we tackle the problem of person search, which is a challenging
task consisted of pedestrian detection and person re-identification~(re-ID).
Instead of sharing representations in a single joint model, we find that
separating detector and re-ID feature extraction yields better performance. In
order to extract more representative features for each identity, we segment out
the foreground person from the original image patch. We propose a simple yet
effective re-ID method, which models foreground person and original image
patches individually, and obtains enriched representations from two separate
CNN streams. From the experiments on two standard person search benchmarks of
CUHK-SYSU and PRW, we achieve mAP of $83.0\%$ and $32.6\%$ respectively,
surpassing the state of the art by a large margin (more than 5pp).
Conditional Infilling GANs for Data Augmentation in Mammogram Classification
Authors: Eric Wu, Kevin Wu, David Cox, William Lotter
Date: 2018-07-21 00:00:00
Tasks: Breast Cancer Detection, Cancer Classification, Classification, Data Augmentation, General Classification

Deep learning approaches to breast cancer detection in mammograms have
recently shown promising results. However, such models are constrained by the
limited size of publicly available mammography datasets, in large part due to
privacy concerns and the high cost of generating expert annotations. Limited
dataset size is further exacerbated by substantial class imbalance since
"normal" images dramatically outnumber those with findings. Given the rapid
progress of generative models in synthesizing realistic images, and the known
effectiveness of simple data augmentation techniques (e.g. horizontal
flipping), we ask if it is possible to synthetically augment mammogram datasets
using generative adversarial networks (GANs). We train a class-conditional GAN
to perform contextual in-filling, which we then use to synthesize lesions onto
healthy screening mammograms. First, we show that GANs are capable of
generating high-resolution synthetic mammogram patches. Next, we experimentally
evaluate using the augmented dataset to improve breast cancer classification
performance. We observe that a ResNet-50 classifier trained with GAN-augmented
training data produces a higher AUROC compared to the same model trained only
on traditionally augmented data, demonstrating the potential of our approach.
Streaming Methods for Restricted Strongly Convex Functions with Applications to Prototype Selection
Authors: Karthik S. Gurumoorthy, Amit Dhurandhar
Date: 2018-07-21 00:00:00
Tasks: Prototype Selection

In this paper, we show that if the optimization function is
restricted-strongly-convex (RSC) and restricted-smooth (RSM) -- a rich subclass
of weakly submodular functions -- then a streaming algorithm with constant
factor approximation guarantee is possible. More generally, our results are
applicable to any monotone weakly submodular function with submodularity ratio
bounded from above. This (positive) result which provides a sufficient
condition for having a constant factor streaming guarantee for weakly
submodular functions may be of special interest given the recent negative
result (Elenberg et al., 2017) for the general class of weakly submodular
functions. We apply our streaming algorithms for creating compact synopsis of
large complex datasets, by selecting $m$ representative elements, by optimizing
a suitable RSC and RSM objective function. Above results hold even with
additional constraints such as learning non-negative weights, for
interpretability, for each selected element indicative of its importance. We
empirically evaluate our algorithms on two real datasets: MNIST- a handwritten
digits dataset and Letters- a UCI dataset containing the alphabet written in
different fonts and styles. We observe that our algorithms are orders of
magnitude faster than the state-of-the-art streaming algorithm for weakly
submodular functions and with our main algorithm still providing equally good
solutions in practice.
Phonetic-and-Semantic Embedding of Spoken Words with Applications in Spoken Content Retrieval
Authors: Yi-Chen Chen, Sung-Feng Huang, Chia-Hao Shen, Hung-Yi Lee, Lin-shan Lee
Date: 2018-07-21 00:00:00
Tasks: Retrieval

Word embedding or Word2Vec has been successful in offering semantics for text
words learned from the context of words. Audio Word2Vec was shown to offer
phonetic structures for spoken words (signal segments for words) learned from
signals within spoken words. This paper proposes a two-stage framework to
perform phonetic-and-semantic embedding on spoken words considering the context
of the spoken words. Stage 1 performs phonetic embedding with speaker
characteristics disentangled. Stage 2 then performs semantic embedding in
addition. We further propose to evaluate the phonetic-and-semantic nature of
the audio embeddings obtained in Stage 2 by parallelizing with text embeddings.
In general, phonetic structure and semantics inevitably disturb each other. For
example the words "brother" and "sister" are close in semantics but very
different in phonetic structure, while the words "brother" and "bother" are in
the other way around. But phonetic-and-semantic embedding is attractive, as
shown in the initial experiments on spoken document retrieval. Not only spoken
documents including the spoken query can be retrieved based on the phonetic
structures, but spoken documents semantically related to the query but not
including the query can also be retrieved based on the semantics.
Learning Optimal Resource Allocations in Wireless Systems
Authors: Mark Eisen, Clark Zhang, Luiz. F. O. Chamon, Daniel D. Lee, Alejandro Ribeiro
Date: 2018-07-21 00:00:00

This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constraint, motivating the development of learning methodologies to attempt their solution. To handle stochastic constraints, training is undertaken in the dual domain. It is shown that this can be done with small loss of optimality when using near-universal learning parameterizations. In particular, since deep neural networks (DNN) are near-universal their use is advocated and explored. DNNs are trained here with a model-free primal-dual method that simultaneously learns a DNN parametrization of the resource allocation policy and optimizes the primal and dual variables. Numerical simulations demonstrate the strong performance of the proposed approach on a number of common wireless resource allocation problems.
A Pipeline for Creative Visual Storytelling
Authors: Stephanie M. Lukin, Reginald Hobbs, Clare R. Voss
Date: 2018-07-21 00:00:00
Proceeding: WS 2018 6
Tasks: Visual Storytelling

Computational visual storytelling produces a textual description of events
and interpretations depicted in a sequence of images. These texts are made
possible by advances and cross-disciplinary approaches in natural language
processing, generation, and computer vision. We define a computational creative
visual storytelling as one with the ability to alter the telling of a story
along three aspects: to speak about different environments, to produce
variations based on narrative goals, and to adapt the narrative to the
audience. These aspects of creative storytelling and their effect on the
narrative have yet to be explored in visual storytelling. This paper presents a
pipeline of task-modules, Object Identification, Single-Image Inferencing, and
Multi-Image Narration, that serve as a preliminary design for building a
creative visual storyteller. We have piloted this design for a sequence of
images in an annotation task. We present and analyze the collected corpus and
describe plans towards automation.