pwc-archive/papers-with-abstracts huggingface dataset:
Salient Object Detection via High-to-Low Hierarchical Context Aggregation
Authors: Yun Liu, Yu Qiu, Le Zhang, Jia-Wang Bian, Guang-Yu Nie, Ming-Ming Cheng
Date: 2018-12-28 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Saliency Prediction, Salient Object Detection, Self-Learning, Vocal Bursts Intensity Prediction

Recent progress on salient object detection mainly aims at exploiting how to
effectively integrate convolutional side-output features in convolutional
neural networks (CNN). Based on this, most of the existing state-of-the-art
saliency detectors design complex network structures to fuse the side-output
features of the backbone feature extraction networks. However, should the
fusion strategies be more and more complex for accurate salient object
detection? In this paper, we observe that the contexts of a natural image can
be well expressed by a high-to-low self-learning of side-output convolutional
features. As we know, the contexts of an image usually refer to the global
structures, and the top layers of CNN usually learn to convey global
information. On the other hand, it is difficult for the intermediate
side-output features to express contextual information. Here, we design an
hourglass network with intermediate supervision to learn contextual features in
a high-to-low manner. The learned hierarchical contexts are aggregated to
generate the hybrid contextual expression for an input image. At last, the
hybrid contextual features can be used for accurate saliency estimation. We
extensively evaluate our method on six challenging saliency datasets, and our
simple method achieves state-of-the-art performance under various evaluation
metrics. Code will be released upon paper acceptance.
Wikibook-Bot - Automatic Generation of a Wikipedia Book
Authors: Shahar Admati, Lior Rokach, Bracha Shapira
Date: 2018-12-28 00:00:00
Tasks: Articles, BIG-bench Machine Learning, Clustering

A Wikipedia book (known as Wikibook) is a collection of Wikipedia articles on
a particular theme that is organized as a book. We propose Wikibook-Bot, a
machine-learning based technique for automatically generating high quality
Wikibooks based on a concept provided by the user. In order to create the
Wikibook we apply machine learning algorithms to the different steps of the
proposed technique. Firs, we need to decide whether an article belongs to a
specific Wikibook - a classification task. Then, we need to divide the chosen
articles into chapters - a clustering task - and finally, we deal with the
ordering task which includes two subtasks: order articles within each chapter
and order the chapters themselves. We propose a set of structural, text-based
and unique Wikipedia features, and we show that by using these features, a
machine learning classifier can successfully address the above challenges. The
predictive performance of the proposed method is evaluated by comparing the
auto-generated books to existing 407 Wikibooks which were manually generated by
humans. For all the tasks we were able to obtain high and statistically
significant results when comparing the Wikibook-bot books to books that were
manually generated by Wikipedia contributors
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation
Authors: Xuan Liu, Xiaoguang Wang, Stan Matwin
Date: 2018-12-28 00:00:00
Tasks: Ethics, Knowledge Distillation, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Deep Neural Networks have achieved huge success at a wide spectrum of
applications from language modeling, computer vision to speech recognition.
However, nowadays, good performance alone is not sufficient to satisfy the
needs of practical deployment where interpretability is demanded for cases
involving ethics and mission critical applications. The complex models of Deep
Neural Networks make it hard to understand and reason the predictions, which
hinders its further progress. To tackle this problem, we apply the Knowledge
Distillation technique to distill Deep Neural Networks into decision trees in
order to attain good performance and interpretability simultaneously. We
formulate the problem at hand as a multi-output regression problem and the
experiments demonstrate that the student model achieves significantly better
accuracy performance (about 1\% to 5\%) than vanilla decision trees at the same
level of tree depth. The experiments are implemented on the TensorFlow platform
to make it scalable to big datasets. To the best of our knowledge, we are the
first to distill Deep Neural Networks into vanilla decision trees on
multi-class datasets.
Deep Learning for Automated Classification of Tuberculosis-Related Chest X-Ray: Dataset Specificity Limits Diagnostic Performance Generalizability
Authors: Seelwan Sathitratanacheewin, Krit Pongpirul
Date: 2018-11-13 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, General Classification, Specificity

Machine learning has been an emerging tool for various aspects of infectious
diseases including tuberculosis surveillance and detection. However, WHO
provided no recommendations on using computer-aided tuberculosis detection
software because of the small number of studies, methodological limitations,
and limited generalizability of the findings. To quantify the generalizability
of the machine-learning model, we developed a Deep Convolutional Neural Network
(DCNN) model using a TB-specific CXR dataset of one population (National
Library of Medicine Shenzhen No.3 Hospital) and tested it with non-TB-specific
CXR dataset of another population (National Institute of Health Clinical
Centers). The findings suggested that a supervised deep learning model
developed by using the training dataset from one population may not have the
same diagnostic performance in another population. Technical specification of
CXR images, disease severity distribution, overfitting, and overdiagnosis
should be examined before implementation in other settings.
Spatiotemporal Data Fusion for Precipitation Nowcasting
Authors: Vladimir Ivashkin, Vadim Lebedev
Date: 2018-12-28 00:00:00

Precipitation nowcasting using neural networks and ground-based radars has
become one of the key components of modern weather prediction services, but it
is limited to the regions covered by ground-based radars. Truly global
precipitation nowcasting requires fusion of radar and satellite observations.
We propose the data fusion pipeline based on computer vision techniques,
including novel inpainting algorithm with soft masking.
On Computation and Generalization of GANs with Spectrum Control
Authors: Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang, Tuo Zhao
Date: 2018-12-28 00:00:00

Generative Adversarial Networks (GANs), though powerful, is hard to train.
Several recent works (brock2016neural,miyato2018spectral) suggest that
controlling the spectra of weight matrices in the discriminator can
significantly improve the training of GANs. Motivated by their discovery, we
propose a new framework for training GANs, which allows more flexible spectrum
control (e.g., making the weight matrices of the discriminator have slow
singular value decays). Specifically, we propose a new reparameterization
approach for the weight matrices of the discriminator in GANs, which allows us
to directly manipulate the spectra of the weight matrices through various
regularizers and constraints, without intensively computing singular value
decompositions. Theoretically, we further show that the spectrum control
improves the generalization ability of GANs. Our experiments on CIFAR-10,
STL-10, and ImageNet datasets confirm that compared to other methods, our
proposed method is capable of generating images with competitive quality by
utilizing spectral normalization and encouraging the slow singular value decay.
Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model
Authors: Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu
Date: 2018-12-28 00:00:00
Tasks: model

This paper proposes the divergence triangle as a framework for joint training
of generator model, energy-based model and inference model. The divergence
triangle is a compact and symmetric (anti-symmetric) objective function that
seamlessly integrates variational learning, adversarial learning, wake-sleep
algorithm, and contrastive divergence in a unified probabilistic formulation.
This unification makes the processes of sampling, inference, energy evaluation
readily available without the need for costly Markov chain Monte Carlo methods.
Our experiments demonstrate that the divergence triangle is capable of learning
(1) an energy-based model with well-formed energy landscape, (2) direct
sampling in the form of a generator network, and (3) feed-forward inference
that faithfully reconstructs observed as well as synthesized data. The
divergence triangle is a robust training method that can learn from incomplete
data.
A Framework for Automated Pop-song Melody Generation with Piano Accompaniment Arrangement
Authors: Ziyu Wang, Gus Xia
Date: 2018-12-28 00:00:00

We contribute a pop-song automation framework for lead melody generation and
accompaniment arrangement. The framework reflects the major procedures of human
music composition, generating both lead melody and piano accompaniment by a
unified strategy. Specifically, we take chord progression as an input and
propose three models to generate a structured melody with piano accompaniment
textures. First, the harmony alternation model transforms a raw input chord
progression to an altered one to better fit the specified music style. Second,
the melody generation model generates the lead melody and other voices (melody
lines) of the accompaniment using seasonal ARMA (Autoregressive Moving Average)
processes. Third, the melody integration model integrates melody lines (voices)
together as the final piano accompaniment. We evaluate the proposed framework
using subjective listening tests. Experimental results show that the generated
melodies are rated significantly higher than the ones generated by
bi-directional LSTM, and our accompaniment arrangement result is comparable
with a state-of-the-art commercial software, Band in a Box.
Deep Convolutional Neural Networks in the Face of Caricature: Identity and Image Revealed
Authors: Matthew Q. Hill, Connor J. Parde, Carlos D. Castillo, Y. Ivette Colon, Rajeev Ranjan, Jun-Cheng Chen, Volker Blanz, Alice J. O'Toole
Date: 2018-12-28 00:00:00
Tasks: Caricature, Face Recognition, Image-Variation, Network Identification

Real-world face recognition requires an ability to perceive the unique
features of an individual face across multiple, variable images. The primate
visual system solves the problem of image invariance using cascades of neurons
that convert images of faces into categorical representations of facial
identity. Deep convolutional neural networks (DCNNs) also create generalizable
face representations, but with cascades of simulated neurons. DCNN
representations can be examined in a multidimensional "face space", with
identities and image parameters quantified via their projections onto the axes
that define the space. We examined the organization of viewpoint, illumination,
gender, and identity in this space. We show that the network creates a highly
organized, hierarchically nested, face similarity structure in which
information about face identity and imaging characteristics coexist. Natural
image variation is accommodated in this hierarchy, with face identity nested
under gender, illumination nested under identity, and viewpoint nested under
illumination. To examine identity, we caricatured faces and found that network
identification accuracy increased with caricature level, and--mimicking human
perception--a caricatured distortion of a face "resembled" its veridical
counterpart. Caricatures improved performance by moving the identity away from
other identities in the face space and minimizing the effects of illumination
and viewpoint. Deep networks produce face representations that solve
long-standing computational problems in generalized face recognition. They also
provide a unitary theoretical framework for reconciling decades of behavioral
and neural results that emphasized either the image or the object/face in
representations, without understanding how a neural code could seamlessly
accommodate both.
Knowledge Representation Learning: A Quantitative Review
Authors: Yankai Lin, Xu Han, Ruobing Xie, Zhiyuan Liu, Maosong Sun
Date: 2018-12-28 00:00:00
Tasks: General Classification, Information Retrieval, Knowledge Graph Completion, Language Modeling, Language Modelling, Question Answering, Recommendation Systems, Relation Extraction, Representation Learning, Retrieval, Triple Classification

Knowledge representation learning (KRL) aims to represent entities and
relations in knowledge graph in low-dimensional semantic space, which have been
widely used in massive knowledge-driven tasks. In this article, we introduce
the reader to the motivations for KRL, and overview existing approaches for
KRL. Afterwards, we extensively conduct and quantitative comparison and
analysis of several typical KRL methods on three evaluation tasks of knowledge
acquisition including knowledge graph completion, triple classification, and
relation extraction. We also review the real-world applications of KRL, such as
language modeling, question answering, information retrieval, and recommender
systems. Finally, we discuss the remaining challenges and outlook the future
directions for KRL. The codes and datasets used in the experiments can be found
in https://github.com/thunlp/OpenKE.
Open-endedness in AI systems, cellular evolution and intellectual discussions
Authors: Kushal Shah
Date: 2018-12-28 00:00:00

One of the biggest challenges that artificial intelligence (AI) research is
facing in recent times is to develop algorithms and systems that are not only
good at performing a specific intelligent task but also good at learning a very
diverse of skills somewhat like humans do. In other words, the goal is to be
able to mimic biological evolution which has produced all the living species on
this planet and which seems to have no end to its creativity. The process of
intellectual discussions is also somewhat similar to biological evolution in
this regard and is responsible for many of the innovative discoveries and
inventions that scientists and engineers have made in the past. In this paper,
we present an information theoretic analogy between the process of discussions
and the molecular dynamics within a cell, showing that there is a common
process of information exchange at the heart of these two seemingly different
processes, which can perhaps help us in building AI systems capable of
open-ended innovation. We also discuss the role of consciousness in this
process and present a framework for the development of open-ended AI systems.
Identifying Computer-Translated Paragraphs using Coherence Features
Authors: Hoang-Quoc Nguyen-Son, Ngoc-Dung T. Tieu, Huy H. Nguyen, Junichi Yamagishi, Isao Echizen
Date: 2018-12-28 00:00:00
Proceeding: PACLIC 2018 12
Tasks: Paper generation, Translation

We have developed a method for extracting the coherence features from a
paragraph by matching similar words in its sentences. We conducted an
experiment with a parallel German corpus containing 2000 human-created and 2000
machine-translated paragraphs. The result showed that our method achieved the
best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is
compared with previous methods on various computer-generated text including
translation and paper generation (best accuracy = 67.9%, equal error rate =
32.0%). Experiments on Dutch, another rich resource language, and a low
resource one (Japanese) attained similar performances. It demonstrated the
efficiency of the coherence features at distinguishing computer-translated from
human-created paragraphs on diverse languages.
InstaGAN: Instance-aware Image-to-Image Translation
Authors: Sangwoo Mo, Minsu Cho, Jinwoo Shin
Date: 2018-12-28 00:00:00
Tasks: GPU, Image-to-Image Translation, Semantic Segmentation, Translation, Unsupervised Image-To-Image Translation

Unsupervised image-to-image translation has gained considerable attention due
to the recent impressive progress based on generative adversarial networks
(GANs). However, previous methods often fail in challenging cases, in
particular, when an image has multiple target instances and a translation task
involves significant changes in shape, e.g., translating pants to skirts in
fashion images. To tackle the issues, we propose a novel method, coined
instance-aware GAN (InstaGAN), that incorporates the instance information
(e.g., object segmentation masks) and improves multi-instance transfiguration.
The proposed method translates both an image and the corresponding set of
instance attributes while maintaining the permutation invariance property of
the instances. To this end, we introduce a context preserving loss that
encourages the network to learn the identity function outside of target
instances. We also propose a sequential mini-batch inference/training technique
that handles multiple instances with a limited GPU memory and enhances the
network to generalize better for multiple instances. Our comparative evaluation
demonstrates the effectiveness of the proposed method on different image
datasets, in particular, in the aforementioned challenging cases. Code and
results are available in https://github.com/sangwoomo/instagan
Coarse-to-fine Semantic Segmentation from Image-level Labels
Authors: Longlong Jing, Yu-cheng Chen, YingLi Tian
Date: 2018-12-28 00:00:00
Tasks: Foreground Segmentation, Object, Segmentation, Semantic Segmentation

Deep neural network-based semantic segmentation generally requires
large-scale cost extensive annotations for training to obtain better
performance. To avoid pixel-wise segmentation annotations which are needed for
most methods, recently some researchers attempted to use object-level labels
(e.g. bounding boxes) or image-level labels (e.g. image categories). In this
paper, we propose a novel recursive coarse-to-fine semantic segmentation
framework based on only image-level category labels. For each image, an initial
coarse mask is first generated by a convolutional neural network-based
unsupervised foreground segmentation model and then is enhanced by a graph
model. The enhanced coarse mask is fed to a fully convolutional neural network
to be recursively refined. Unlike existing image-level label-based semantic
segmentation methods which require to label all categories for images contain
multiple types of objects, our framework only needs one label for each image
and can handle images contains multi-category objects. With only trained on
ImageNet, our framework achieves comparable performance on PASCAL VOC dataset
as other image-level label-based state-of-the-arts of semantic segmentation.
Furthermore, our framework can be easily extended to foreground object
segmentation task and achieves comparable performance with the state-of-the-art
supervised methods on the Internet Object dataset.
Hypergraph Clustering: A Modularity Maximization Approach
Authors: Tarun Kumar, Sankaran Vaidyanathan, Harini Ananthapadmanabhan, Srinivasan Parthasarathy, Balaraman Ravindran
Date: 2018-12-28 00:00:00
Tasks: Clustering

Clustering on hypergraphs has been garnering increased attention with
potential applications in network analysis, VLSI design and computer vision,
among others. In this work, we generalize the framework of modularity
maximization for clustering on hypergraphs. To this end, we introduce a
hypergraph null model, analogous to the configuration model on undirected
graphs, and a node-degree preserving reduction to work with this model. This is
used to define a modularity function that can be maximized using the popular
and fast Louvain algorithm. We additionally propose a refinement over this
clustering, by reweighting cut hyperedges in an iterative fashion. The efficacy
and efficiency of our methods are demonstrated on several real-world datasets.
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Authors: Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
Date: 2018-12-28 00:00:00
Proceeding: ACL 2019 7
Tasks: Language Modeling, Language Modelling, Natural Language Understanding, Sentence, Transfer Learning

Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo's pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research.
Signal Classification under structure sparsity constraints
Authors: Tiep Huu Vu
Date: 2018-12-28 00:00:00
Tasks: Classification, General Classification, Object

Object Classification is a key direction of research in signal and image
processing, computer vision and artificial intelligence. The goal is to come up
with algorithms that automatically analyze images and put them in predefined
categories. This dissertation focuses on the theory and application of sparse
signal processing and learning algorithms for image processing and computer
vision, especially object classification problems. A key emphasis of this work
is to formulate novel optimization problems for learning dictionary and
structured sparse representations. Tractable solutions are proposed
subsequently for the corresponding optimization problems.
  An important goal of this dissertation is to demonstrate the wide
applications of these algorithmic tools for real-world applications. To that
end, we explored important problems in the areas of:
  1. Medical imaging: histopathological images acquired from mammalian tissues,
human breast tissues, and human brain tissues.
  2. Low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture
radar: detecting bombs and mines buried under rough surfaces.
  3. General object classification: face, flowers, objects, dogs, indoor
scenes, etc.