pwc-archive/papers-with-abstracts huggingface dataset:
Frank-Wolfe Algorithm for the Exact Sparse Problem
Authors: Farah Cherfaoui, Valentin Emiya, Liva Ralaivola, Sandrine Anthoine
Date: 2018-12-18 00:00:00

In this paper, we study the properties of the Frank-Wolfe algorithm to solve
the \ExactSparse reconstruction problem. We prove that when the dictionary is
quasi-incoherent, at each iteration, the Frank-Wolfe algorithm picks up an atom
indexed by the support. We also prove that when the dictionary is
quasi-incoherent, there exists an iteration beyond which the algorithm
converges exponentially fast.
D{é}tection de locuteurs dans les s{é}ries TV
Authors: Xavier Bost, Georges Linares
Date: 2018-12-18 00:00:00
Tasks: Clustering, speaker-diarization, Speaker Diarization

Speaker diarization of audio streams turns out to be particularly challenging
when applied to fictional films, where many characters talk in various acoustic
conditions (background music, sound effects, variations in intonation...).
Despite this acoustic variability, such movies exhibit specific visual
patterns, particularly within dialogue scenes. In this paper, we introduce a
two-step method to achieve speaker diarization in TV series: speaker
diarization is first performed locally within scenes visually identified as
dialogues; then, the hypothesized local speakers are compared to each other
during a second clustering process in order to detect recurring speakers: this
second stage of clustering is subject to the constraint that the different
speakers involved in the same dialogue have to be assigned to different
clusters. The performances of our approach are compared to those obtained by
standard speaker diarization tools applied to the same data.
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
Authors: Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
Date: 2018-12-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Detection, 3D Object Detection From Stereo Images, Autonomous Driving, Depth Estimation, object-detection, Object Detection

3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise but expensive LiDAR technology. Approaches based on cheaper monocular or stereo imagery data have, until now, resulted in drastically lower accuracies --- a gap that is commonly attributed to poor image-based depth estimation. However, in this paper we argue that it is not the quality of the data but its representation that accounts for the majority of the difference. Taking the inner workings of convolutional neural networks into consideration, we propose to convert image-based depth maps to pseudo-LiDAR representations --- essentially mimicking the LiDAR signal. With this representation we can apply different existing LiDAR-based detection algorithms. On the popular KITTI benchmark, our approach achieves impressive improvements over the existing state-of-the-art in image-based performance --- raising the detection accuracy of objects within the 30m range from the previous state-of-the-art of 22% to an unprecedented 74%. At the time of submission our algorithm holds the highest entry on the KITTI 3D object detection leaderboard for stereo-image-based approaches. Our code is publicly available at https://github.com/mileyan/pseudo_lidar.
SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network
Authors: Kwanyoung Kim, Se Young Chun
Date: 2018-12-18 00:00:00
Tasks: Edge Detection, Image Super-Resolution, SSIM, Super-Resolution

Deep learning based single image super-resolution (SR) methods have been
rapidly evolved over the past few years and have yielded state-of-the-art
performances over conventional methods. Since these methods usually minimized
l1 loss between the output SR image and the ground truth image, they yielded
very high peak signal-to-noise ratio (PSNR) that is inversely proportional to
these losses. Unfortunately, minimizing these losses inevitably lead to blurred
edges due to averaging of plausible solutions. Recently, SRGAN was proposed to
avoid this average effect by minimizing perceptual losses instead of l1 loss
and it yielded perceptually better SR images (or images with sharp edges) at
the price of lowering PSNR. In this paper, we propose SREdgeNet, edge enhanced
single image SR network, that was inspired by conventional SR theories so that
average effect could be avoided not by changing the loss, but by changing the
SR network property with the same l1 loss. Our SREdgeNet consists of 3
sequential deep neural network modules: the first module is any
state-of-the-art SR network and we selected a variant of EDSR. The second
module is any edge detection network taking the output of the first SR module
as an input and we propose DenseEdgeNet for this module. Lastly, the third
module is merging the outputs of the first and second modules to yield edge
enhanced SR image and we propose MergeNet for this module. Qualitatively, our
proposed method yielded images with sharp edges compared to other
state-of-the-art SR methods. Quantitatively, our SREdgeNet yielded
state-of-the-art performance in terms of structural similarity (SSIM) while
maintained comparable PSNR for x8 enlargement.
Toward Multimodal Model-Agnostic Meta-Learning
Authors: Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim
Date: 2018-12-18 00:00:00
Tasks: Diversity, Few-Shot Image Classification, image-classification, Image Classification, Meta-Learning, model, Reinforcement Learning

Gradient-based meta-learners such as MAML are able to learn a meta-prior from
similar tasks to adapt to novel tasks from the same distribution with few
gradient updates. One important limitation of such frameworks is that they seek
a common initialization shared across the entire task distribution,
substantially limiting the diversity of the task distributions that they are
able to learn from. In this paper, we augment MAML with the capability to
identify tasks sampled from a multimodal task distribution and adapt quickly
through gradient updates. Specifically, we propose a multimodal MAML algorithm
that is able to modulate its meta-learned prior according to the identified
task, allowing faster adaptation. We evaluate the proposed model on a diverse
set of problems including regression, few-shot image classification, and
reinforcement learning. The results demonstrate the effectiveness of our model
in modulating the meta-learned prior in response to the characteristics of
tasks sampled from a multimodal distribution.
Explaining Neural Networks Semantically and Quantitatively
Authors: Runjin Chen, Hao Chen, Ge Huang, Jie Ren, Quanshi Zhang
Date: 2018-12-18 00:00:00
Proceeding: ICCV 2019 10

This paper presents a method to explain the knowledge encoded in a
convolutional neural network (CNN) quantitatively and semantically. The
analysis of the specific rationale of each prediction made by the CNN presents
a key issue of understanding neural networks, but it is also of significant
practical values in certain applications. In this study, we propose to distill
knowledge from the CNN into an explainable additive model, so that we can use
the explainable model to provide a quantitative explanation for the CNN
prediction. We analyze the typical bias-interpreting problem of the explainable
model and develop prior losses to guide the learning of the explainable
additive model. Experimental results have demonstrated the effectiveness of our
method.
Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images
Authors: Jiechao Ma, Xiang Li, Hongwei Li, Bjoern H. Menze, Sen Liang, Rongguo Zhang, Wei-Shi Zheng
Date: 2018-12-18 00:00:00
Tasks: Computed Tomography (CT), Finding Pulmonary Nodules In Large-Scale Ct Images

Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed tomography (CT) images. In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D single-shot detector (SSD) called group-attention SSD (GA-SSD). We find that group convolution is effective in extracting rich context information between continuous slices, and attention network can learn the target features automatically. We collected a large-scale dataset that contained 4146 CT scans with annotations of varying types and sizes of PNs (even PNs smaller than 3mm were annotated). To the best of our knowledge, this dataset is the largest cohort with relatively complete annotations for PNs detection. Our experimental results show that the proposed group-attention SSD outperforms the classic SSD framework as well as the state-of-the-art 3DCNN, especially on some challenging lesion types.
Autoencoder Based Architecture For Fast & Real Time Audio Style Transfer
Authors: Dhruv Ramani, Samarjit Karmakar, Anirban Panda, Asad Ahmed, Pratham Tangri
Date: 2018-12-18 00:00:00
Tasks: Style Transfer

Recently, there has been great interest in the field of audio style transfer,
where a stylized audio is generated by imposing the style of a reference audio
on the content of a target audio. We improve on the current approaches which
use neural networks to extract the content and the style of the audio signal
and propose a new autoencoder based architecture for the task. This network
generates a stylized audio for a content audio in a single forward pass. The
proposed network architecture proves to be advantageous over the quality of
audio produced and the time taken to train the network. The network is
experimented on speech signals to confirm the validity of our proposal.
Attention-based Recurrent Neural Network for Urban Vehicle Trajectory Prediction
Authors: Seongjin Choi, Jiwon Kim, Hwasoo Yeo
Date: 2018-12-18 00:00:00
Tasks: Prediction, Trajectory Prediction

With the increasing deployment of diverse positioning devices and location-based services, a huge amount of spatial and temporal information has been collected and accumulated as trajectory data. Among many applications, trajectory-based location prediction is gaining increasing attention because of its potential to improve the performance of many applications in multiple domains. This research focuses on trajectory sequence prediction methods using trajectory data obtained from the vehicles in urban traffic network. As Recurrent Neural Network(RNN) model is previously proposed, we propose an improved method of Attention-based Recurrent Neural Network model(ARNN) for urban vehicle trajectory prediction. We introduce attention mechanism into urban vehicle trajectory prediction to explain the impact of network-level traffic state information. The model is evaluated using the Bluetooth data of private vehicles collected in Brisbane, Australia with 5 metrics which are widely used in the sequence modeling. The proposed ARNN model shows significant performance improvement compared to the existing RNN models considering not only the cells to be visited but also the alignment of the cells in sequence.
Interactive Naming for Explaining Deep Neural Networks: A Formative Study
Authors: Mandana Hamidi-Haines, Zhongang Qi, Alan Fern, Fuxin Li, Prasad Tadepalli
Date: 2018-12-18 00:00:00
Tasks: General Classification

We consider the problem of explaining the decisions of deep neural networks
for image recognition in terms of human-recognizable visual concepts. In
particular, given a test set of images, we aim to explain each classification
in terms of a small number of image regions, or activation maps, which have
been associated with semantic concepts by a human annotator. This allows for
generating summary views of the typical reasons for classifications, which can
help build trust in a classifier and/or identify example types for which the
classifier may not be trusted. For this purpose, we developed a user interface
for "interactive naming," which allows a human annotator to manually cluster
significant activation maps in a test set into meaningful groups called "visual
concepts". The main contribution of this paper is a systematic study of the
visual concepts produced by five human annotators using the interactive naming
interface. In particular, we consider the adequacy of the concepts for
explaining the classification of test-set images, correspondence of the
concepts to activations of individual neurons, and the inter-annotator
agreement of visual concepts. We find that a large fraction of the activation
maps have recognizable visual concepts, and that there is significant agreement
between the different annotators about their denotations. Our work is an
exploratory study of the interplay between machine learning and human
recognition mediated by visualizations of the results of learning.
Recurrent Calibration Network for Irregular Text Recognition
Authors: Yunze Gao, Yingying Chen, Jinqiao Wang, Zhen Lei, Xiao-Yu Zhang, Hanqing Lu
Date: 2018-12-18 00:00:00
Tasks: Irregular Text Recognition, Scene Text Recognition

Scene text recognition has received increased attention in the research
community. Text in the wild often possesses irregular arrangements, typically
including perspective text, curved text, oriented text. Most existing methods
are hard to work well for irregular text, especially for severely distorted
text. In this paper, we propose a novel Recurrent Calibration Network (RCN) for
irregular scene text recognition. The RCN progressively calibrates the
irregular text to boost the recognition performance. By decomposing the
calibration process into multiple steps, the irregular text can be calibrated
to normal one step by step. Besides, in order to avoid the accumulation of lost
information caused by inaccurate transformation, we further design a
fiducial-point refinement structure to keep the integrity of text during the
recurrent process. Instead of the calibrated images, the coordinates of
fiducial points are tracked and refined, which implicitly models the
transformation information. Based on the refined fiducial points, we estimate
the transformation parameters and sample from the original image at each step.
In this way, the original character information is preserved until the final
transformation. Such designs lead to optimal calibration results to boost the
performance of succeeding recognition. Extensive experiments on challenging
datasets demonstrate the superiority of our method, especially on irregular
benchmarks.
Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling
Authors: Karan Aggarwal, Onur Atan, Ahmed Farahat, Chi Zhang, Kosta Ristovski, Chetan Gupta
Date: 2018-12-18 00:00:00
Tasks: Multi-Task Learning, Prediction

One of the key challenges in predictive maintenance is to predict the
impending downtime of an equipment with a reasonable prediction horizon so that
countermeasures can be put in place. Classically, this problem has been posed
in two different ways which are typically solved independently: (1) Remaining
useful life (RUL) estimation as a long-term prediction task to estimate how
much time is left in the useful life of the equipment and (2) Failure
prediction (FP) as a short-term prediction task to assess the probability of a
failure within a pre-specified time window. As these two tasks are related,
performing them separately is sub-optimal and might results in inconsistent
predictions for the same equipment. In order to alleviate these issues, we
propose two methods: Deep Weibull model (DW-RNN) and multi-task learning
(MTL-RNN). DW-RNN is able to learn the underlying failure dynamics by fitting
Weibull distribution parameters using a deep neural network, learned with a
survival likelihood, without training directly on each task. While DW-RNN makes
an explicit assumption on the data distribution, MTL-RNN exploits the implicit
relationship between the long-term RUL and short-term FP tasks to learn the
underlying distribution. Additionally, both our methods can leverage the
non-failed equipment data for RUL estimation. We demonstrate that our methods
consistently outperform baseline RUL methods that can be used for FP while
producing consistent results for RUL and FP. We also show that our methods
perform at par with baselines trained on the objectives optimized for either of
the two tasks.
Anomaly Detection and Interpretation using Multimodal Autoencoder and Sparse Optimization
Authors: Yasuhiro Ikeda, Keisuke Ishibashi, Yuusuke Nakano, Keishiro Watanabe, Ryoichi Kawahara
Date: 2018-12-18 00:00:00
Tasks: Anomaly Detection

Automated anomaly detection is essential for managing information and
communications technology (ICT) systems to maintain reliable services with
minimum burden on operators. For detecting varying and continually emerging
anomalies as differences from normal states, learning normal relationships
inherent among cross-domain data monitored from ICT systems is essential.
Deep-learning-based anomaly detection using an autoencoder (AE) is therefore
promising for such complicated learning; however, its interpretation is still
problematic. Since the dimensions of the input data contributing to the
detected anomaly are not directly indicated in an AE, they are not suitable for
localizing anomalies in large ICT systems composed of a huge amount of
equipment. We propose an algorithm using sparse optimization for estimating
contributing dimensions to anomalies detected with AEs. We also propose a
multimodal AE (MAE) for effectively learning the relationships among
cross-domain data, which can induce nonlinearity and differences in
learnability among data types. We evaluated our algorithms with several
datasets including real measured data in comparison with conventional
algorithms and confirmed the superiority of our estimation algorithm in
specifying contributing dimensions of anomalous data and our MAE in detecting
anomalies in cross-domain data.
Globalness Detection in Online Social Network
Authors: Yu-Cheng Lin, Chun-Ming Lai, S. Felix Wu, George A. Barnett
Date: 2018-12-18 00:00:00
Tasks: Classification, General Classification

Classification problems have made significant progress due to the maturity of
artificial intelligence (AI). However, differentiating items from categories
without noticeable boundaries is still a huge challenge for machines -- which
is also crucial for machines to be intelligent.
  In order to study the fuzzy concept on classification, we define and propose
a globalness detection with the four-stage operational flow. We then
demonstrate our framework on Facebook public pages inter-like graph with their
geo-location. Our prediction algorithm achieves high precision (89%) and recall
(88%) of local pages. We evaluate the results on both states and countries
level, finding that the global node ratios are relatively high in those states
(NY, CA) having large and international cities. Several global nodes examples
have also been shown and studied in this paper.
  It is our hope that our results unveil the perfect value from every
classification problem and provide a better understanding of global and local
nodes in Online Social Networks (OSNs).
Hybrid Loss for Learning Single-Image-based HDR Reconstruction
Authors: Kenta Moriwaki, Ryota Yoshihashi, Rei Kawakami, ShaoDi You, Takeshi Naemura
Date: 2018-12-18 00:00:00
Tasks: HDR Reconstruction, Image Reconstruction, Single-Image-Based Hdr Reconstruction

This paper tackles high-dynamic-range (HDR) image reconstruction given only a
single low-dynamic-range (LDR) image as input. While the existing methods focus
on minimizing the mean-squared-error (MSE) between the target and reconstructed
images, we minimize a hybrid loss that consists of perceptual and adversarial
losses in addition to HDR-reconstruction loss. The reconstruction loss instead
of MSE is more suitable for HDR since it puts more weight on both over- and
under- exposed areas. It makes the reconstruction faithful to the input.
Perceptual loss enables the networks to utilize knowledge about objects and
image structure for recovering the intensity gradients of saturated and grossly
quantized areas. Adversarial loss helps to select the most plausible appearance
from multiple solutions. The hybrid loss that combines all the three losses is
calculated in logarithmic space of image intensity so that the outputs retain a
large dynamic range and meanwhile the learning becomes tractable. Comparative
experiments conducted with other state-of-the-art methods demonstrated that our
method produces a leap in image quality.
Deep reinforcement learning for search, recommendation, and online advertising: a survey
Authors: Xiangyu Zhao, Long Xia, Jiliang Tang, Dawei Yin
Date: 2018-12-18 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web. These information seeking techniques, satisfying users' information needs by suggesting users personalized objects (information or services) at the appropriate time and place, play a crucial role in mitigating the information overload problem. With recent great advances in deep reinforcement learning (DRL), there have been increasing interests in developing DRL based information seeking techniques. These DRL based techniques have two key advantages -- (1) they are able to continuously update information seeking strategies according to users' real-time feedback, and (2) they can maximize the expected cumulative long-term reward from users where reward has different definitions according to information seeking applications such as click-through rate, revenue, user satisfaction and engagement. In this paper, we give an overview of deep reinforcement learning for search, recommendation, and online advertising from methodologies to applications, review representative algorithms, and discuss some appealing research directions.
BandNet: A Neural Network-based, Multi-Instrument Beatles-Style MIDI Music Composition Machine
Authors: Yichao Zhou, Wei Chu, Sam Young, Xin Chen
Date: 2018-12-18 00:00:00

In this paper, we propose a recurrent neural network (RNN)-based MIDI music
composition machine that is able to learn musical knowledge from existing
Beatles' songs and generate music in the style of the Beatles with little human
intervention. In the learning stage, a sequence of stylistically uniform,
multiple-channel music samples was modeled by a RNN. In the composition stage,
a short clip of randomly-generated music was used as a seed for the RNN to
start music score prediction. To form structured music, segments of generated
music from different seeds were concatenated together. To improve the quality
and structure of the generated music, we integrated music theory knowledge into
the model, such as controlling the spacing of gaps in the vocal melody,
normalizing the timing of chord changes, and requiring notes to be related to
the song's key (C major, for example). This integration improved the quality of
the generated music as verified by a professional composer. We also conducted a
subjective listening test that showed our generated music was close to original
music by the Beatles in terms of style similarity, professional quality, and
interestingness. Generated music samples are at https://goo.gl/uaLXoB.