pwc-archive/papers-with-abstracts huggingface dataset:
Entropy-difference based stereo error detection
Authors: Subhayan Mukherjee, Irene Cheng, Ram Mohana Reddy Guddeti, Anup Basu
Date: 2017-11-28 00:00:00
Tasks: Binary Classification, Depth Estimation, Stereo Depth Estimation, Stereo Matching, Stereo Matching Hand

Stereo depth estimation is error-prone; hence, effective error detection
methods are desirable. Most such existing methods depend on characteristics of
the stereo matching cost curve, making them unduly dependent on functional
details of the matching algorithm. As a remedy, we propose a novel error
detection approach based solely on the input image and its depth map. Our
assumption is that, entropy of any point on an image will be significantly
higher than the entropy of its corresponding point on the image's depth map. In
this paper, we propose a confidence measure, Entropy-Difference (ED) for stereo
depth estimates and a binary classification method to identify incorrect
depths. Experiments on the Middlebury dataset show the effectiveness of our
method. Our proposed stereo confidence measure outperforms 17 existing measures
in all aspects except occlusion detection. Established metrics such as
precision, accuracy, recall, and area-under-curve are used to demonstrate the
effectiveness of our method.
Nonparametric Independence Screening via Favored Smoothing Bandwidth
Authors: Yang Feng, Yi-Chao Wu, Leonard Stefanski
Date: 2017-11-28 00:00:00
Tasks: Model Selection, regression

We propose a flexible nonparametric regression method for
ultrahigh-dimensional data. As a first step, we propose a fast screening method
based on the favored smoothing bandwidth of the marginal local constant
regression. Then, an iterative procedure is developed to recover both the
important covariates and the regression function. Theoretically, we prove that
the favored smoothing bandwidth based screening possesses the model selection
consistency property. Simulation studies as well as real data analysis show the
competitive performance of the new procedure.
Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting
Authors: Raphael Tang, Jimmy Lin
Date: 2017-10-18 00:00:00
Tasks: Keyword Spotting, speech-recognition, Speech Recognition

We describe Honk, an open-source PyTorch reimplementation of convolutional
neural networks for keyword spotting that are included as examples in
TensorFlow. These models are useful for recognizing "command triggers" in
speech-based interfaces (e.g., "Hey Siri"), which serve as explicit cues for
audio recordings of utterances that are sent to the cloud for full speech
recognition. Evaluation on Google's recently released Speech Commands Dataset
shows that our reimplementation is comparable in accuracy and provides a
starting point for future work on the keyword spotting task.
Adversarial Networks for Prostate Cancer Detection
Authors: Simon Kohl, David Bonekamp, Heinz-Peter Schlemmer, Kaneschka Yaqubi, Markus Hohenfellner, Boris Hadaschik, Jan-Philipp Radtke, Klaus Maier-Hein
Date: 2017-11-28 00:00:00
Tasks: Sensitivity

The large number of trainable parameters of deep neural networks renders them
inherently data hungry. This characteristic heavily challenges the medical
imaging community and to make things even worse, many imaging modalities are
ambiguous in nature leading to rater-dependant annotations that current loss
formulations fail to capture. We propose employing adversarial training for
segmentation networks in order to alleviate aforementioned problems. We learn
to segment aggressive prostate cancer utilizing challenging MRI images of 152
patients and show that the proposed scheme is superior over the de facto
standard in terms of the detection sensitivity and the dice-score for
aggressive prostate cancer. The achieved relative gains are shown to be
particularly pronounced in the small dataset limit.
Exposing Computer Generated Images by Using Deep Convolutional Neural Networks
Authors: Edmar R. S. de Rezende, Guilherme C. S. Ruppert, Antonio Theophilo, Tiago Carvalho
Date: 2017-11-28 00:00:00
Tasks: Transfer Learning

The recent computer graphics developments have upraised the quality of the
generated digital content, astonishing the most skeptical viewer. Games and
movies have taken advantage of this fact but, at the same time, these advances
have brought serious negative impacts like the ones yielded by fakeimages
produced with malicious intents. Digital artists can compose artificial images
capable of deceiving the great majority of people, turning this into a very
dangerous weapon in a timespan currently know as Fake News/Post-Truth" Era. In
this work, we propose a new approach for dealing with the problem of detecting
computer generated images, through the application of deep convolutional
networks and transfer learning techniques. We start from Residual Networks and
develop different models adapted to the binary problem of identifying if an
image was or not computer generated. Differently from the current
state-of-the-art approaches, we don't rely on hand-crafted features, but
provide to the model the raw pixel information, achieving the same 0.97 of
state-of-the-art methods with two main advantages: our methods show more stable
results (depicted by lower variance) and eliminate the laborious and manual
step of specialized features extraction and selection.
Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs
Authors: Rui Zhang, Honglak Lee, Lazaros Polymenakos, Dragomir Radev
Date: 2017-09-12 00:00:00
Tasks: Conversational Response Selection

In this paper, we study the problem of addressee and response selection in
multi-party conversations. Understanding multi-party conversations is
challenging because of complex speaker interactions: multiple speakers exchange
messages with each other, playing different roles (sender, addressee,
observer), and these roles vary across turns. To tackle this challenge, we
propose the Speaker Interaction Recurrent Neural Network (SI-RNN). Whereas the
previous state-of-the-art system updated speaker embeddings only for the
sender, SI-RNN uses a novel dialog encoder to update speaker embeddings in a
role-sensitive way. Additionally, unlike the previous work that selected the
addressee and response separately, SI-RNN selects them jointly by viewing the
task as a sequence prediction problem. Experimental results show that SI-RNN
significantly improves the accuracy of addressee and response selection,
particularly in complex conversations with many speakers and responses to
distant messages many turns in the past.
Structured Probabilistic Pruning for Convolutional Neural Network Acceleration
Authors: Huan Wang, Qiming Zhang, Yuehai Wang, Haoji Hu
Date: 2017-09-20 00:00:00
Tasks: Transfer Learning

In this paper, we propose a novel progressive parameter pruning method for
Convolutional Neural Network acceleration, named Structured Probabilistic
Pruning (SPP), which effectively prunes weights of convolutional layers in a
probabilistic manner. Unlike existing deterministic pruning approaches, where
unimportant weights are permanently eliminated, SPP introduces a pruning
probability for each weight, and pruning is guided by sampling from the pruning
probabilities. A mechanism is designed to increase and decrease pruning
probabilities based on importance criteria in the training process. Experiments
show that, with 4x speedup, SPP can accelerate AlexNet with only 0.3% loss of
top-5 accuracy and VGG-16 with 0.8% loss of top-5 accuracy in ImageNet
classification. Moreover, SPP can be directly applied to accelerate
multi-branch CNN networks, such as ResNet, without specific adaptations. Our 2x
speedup ResNet-50 only suffers 0.8% loss of top-5 accuracy on ImageNet. We
further show the effectiveness of SPP on transfer learning tasks.
Population Based Training of Neural Networks
Authors: Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, Chrisantha Fernando, Koray Kavukcuoglu
Date: 2017-11-27 00:00:00
Tasks: Deep Reinforcement Learning, Machine Translation, Model Selection, Reinforcement Learning

Neural networks dominate the modern machine learning landscape, but their
training and success still suffer from sensitivity to empirical choices of
hyperparameters such as model architecture, loss function, and optimisation
algorithm. In this work we present \emph{Population Based Training (PBT)}, a
simple asynchronous optimisation algorithm which effectively utilises a fixed
computational budget to jointly optimise a population of models and their
hyperparameters to maximise performance. Importantly, PBT discovers a schedule
of hyperparameter settings rather than following the generally sub-optimal
strategy of trying to find a single fixed set to use for the whole course of
training. With just a small modification to a typical distributed
hyperparameter training framework, our method allows robust and reliable
training of models. We demonstrate the effectiveness of PBT on deep
reinforcement learning problems, showing faster wall-clock convergence and
higher final performance of agents by optimising over a suite of
hyperparameters. In addition, we show the same method can be applied to
supervised learning for machine translation, where PBT is used to maximise the
BLEU score directly, and also to training of Generative Adversarial Networks to
maximise the Inception score of generated images. In all cases PBT results in
the automatic discovery of hyperparameter schedules and model selection which
results in stable training and better final performance.
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
Authors: Deqing Sun, Xiaodong Yang, Ming-Yu Liu, Jan Kautz
Date: 2017-09-07 00:00:00
Proceeding: CVPR 2018 6
Tasks: Dense Pixel Correspondence Estimation, Optical Flow Estimation

We present a compact but effective CNN model for optical flow, called
PWC-Net. PWC-Net has been designed according to simple and well-established
principles: pyramidal processing, warping, and the use of a cost volume. Cast
in a learnable feature pyramid, PWC-Net uses the cur- rent optical flow
estimate to warp the CNN features of the second image. It then uses the warped
features and features of the first image to construct a cost volume, which is
processed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in
size and easier to train than the recent FlowNet2 model. Moreover, it
outperforms all published optical flow methods on the MPI Sintel final pass and
KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024x436)
images. Our models are available on https://github.com/NVlabs/PWC-Net.
Role of Deep LSTM Neural Networks And WiFi Networks in Support of Occupancy Prediction in Smart Buildings
Authors: Basheer Qolomany, Ala Al-Fuqaha, Driss Benhaddou, Ajay Gupta
Date: 2017-11-28 00:00:00
Tasks: Time Series, Time Series Analysis

Knowing how many people occupy a building, and where they are located, is a
key component of smart building services. Commercial, industrial and
residential buildings often incorporate systems used to determine occupancy.
However, relatively simple sensor technology and control algorithms limit the
effectiveness of smart building services. In this paper we propose to replace
sensor technology with time series models that can predict the number of
occupants at a given location and time. We use Wi-Fi data sets readily
available in abundance for smart building services and train Auto Regression
Integrating Moving Average (ARIMA) models and Long Short-Term Memory (LSTM)
time series models. As a use case scenario of smart building services, these
models allow forecasting of the number of people at a given time and location
in 15, 30 and 60 minutes time intervals at building as well as Access Point
(AP) level. For LSTM, we build our models in two ways: a separate model for
every time scale, and a combined model for the three time scales. Our
experiments show that LSTM combined model reduced the computational resources
with respect to the number of neurons by 74.48 % for the AP level, and by 67.13
% for the building level. Further, the root mean square error (RMSE) was
reduced by 88.2% - 93.4% for LSTM in comparison to ARIMA for the building
levels models and by 80.9% - 87% for the AP level models.
Parameters Optimization of Deep Learning Models using Particle Swarm Optimization
Authors: Basheer Qolomany, Majdi Maabreh, Ala Al-Fuqaha, Ajay Gupta, Driss Benhaddou
Date: 2017-11-28 00:00:00
Tasks: Deep Learning, Machine Translation

Deep learning has been successfully applied in several fields such as machine
translation, manufacturing, and pattern recognition. However, successful
application of deep learning depends upon appropriately setting its parameters
to achieve high quality results. The number of hidden layers and the number of
neurons in each layer of a deep machine learning network are two key
parameters, which have main influence on the performance of the algorithm.
Manual parameter setting and grid search approaches somewhat ease the users
tasks in setting these important parameters. Nonetheless, these two techniques
can be very time consuming. In this paper, we show that the Particle swarm
optimization (PSO) technique holds great potential to optimize parameter
settings and thus saves valuable computational resources during the tuning
process of deep learning models. Specifically, we use a dataset collected from
a Wi-Fi campus network to train deep learning models to predict the number of
occupants and their locations. Our preliminary experiments indicate that PSO
provides an efficient approach for tuning the optimal number of hidden layers
and the number of neurons in each layer of the deep learning algorithm when
compared to the grid search method. Our experiments illustrate that the
exploration process of the landscape of configurations to find the optimal
parameters is decreased by 77%-85%. In fact, the PSO yields even better
accuracy results.
Learning Face Age Progression: A Pyramid Architecture of GANs
Authors: Hongyu Yang, Di Huang, Yunhong Wang, Anil K. Jain
Date: 2017-11-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Generative Adversarial Network

The two underlying requirements of face age progression, i.e. aging accuracy
and identity permanence, are not well studied in the literature. In this paper,
we present a novel generative adversarial network based approach. It separately
models the constraints for the intrinsic subject-specific characteristics and
the age-specific facial changes with respect to the elapsed time, ensuring that
the generated faces present desired aging effects while simultaneously keeping
personalized properties stable. Further, to generate more lifelike facial
details, high-level age-specific features conveyed by the synthesized face are
estimated by a pyramidal adversarial discriminator at multiple scales, which
simulates the aging effects in a finer manner. The proposed method is
applicable to diverse face samples in the presence of variations in pose,
expression, makeup, etc., and remarkably vivid aging effects are achieved. Both
visual fidelity and quantitative evaluations show that the approach advances
the state-of-the-art.
Robust Optical Flow Estimation in Rainy Scenes
Authors: Ruoteng Li, Robby T. Tan, Loong-Fah Cheong
Date: 2017-04-18 00:00:00
Tasks: Optical Flow Estimation

Optical flow estimation in the rainy scenes is challenging due to background
degradation introduced by rain streaks and rain accumulation effects in the
scene. Rain accumulation effect refers to poor visibility of remote objects due
to the intense rainfall. Most existing optical flow methods are erroneous when
applied to rain sequences because the conventional brightness constancy
constraint (BCC) and gradient constancy constraint (GCC) generally break down
in this situation. Based on the observation that the RGB color channels receive
raindrop radiance equally, we introduce a residue channel as a new data
constraint to reduce the effect of rain streaks. To handle rain accumulation,
our method decomposes the image into a piecewise-smooth background layer and a
high-frequency detail layer. It also enforces the BCC on the background layer
only. Results on both synthetic dataset and real images show that our algorithm
outperforms existing methods on different types of rain sequences. To our
knowledge, this is the first optical flow method specifically dealing with
rain.
Meteorology-Aware Multi-Goal Path Planning for Large-Scale Inspection Missions with Long-Endurance Solar-Powered Aircraft
Authors: Philipp Oettershagen, Julian Förster, Lukas Wirth, Jacques Ambühl, Roland Siegwart
Date: 2017-11-28 00:00:00

Solar-powered aircraft promise significantly increased flight endurance over
conventional aircraft. While this makes them promising candidates for
large-scale aerial inspection missions, their structural fragility necessitates
that adverse weather is avoided using appropriate path planning methods. This
paper therefore presents MetPASS, the Meteorology-aware Path Planning and
Analysis Software for Solar-powered UAVs. MetPASS is the first path planning
framework in the literature that considers all aspects that influence the
safety or performance of solar-powered flight: It avoids environmental risks
(thunderstorms, rain, wind, wind gusts and humidity) and exploits advantageous
regions (high sun radiation or tailwind). It also avoids system risks such as
low battery state of charge and returns safe paths through cluttered terrain.
MetPASS imports weather data from global meteorological models, propagates the
aircraft state through an energetic system model, and then combines both into a
cost function. A combination of dynamic programming techniques and an
A*-search-algorithm with a custom heuristic is leveraged to plan globally
optimal paths in station-keeping, point-to-point or multi-goal aerial
inspection missions with coverage guarantees. A full software implementation
including a GUI is provided. The planning methods are verified using three
missions of ETH Zurich's AtlantikSolar UAV: An 81-hour continuous solar-powered
station-keeping flight, a 4000km Atlantic crossing from Newfoundland to
Portugal, and two multi-glacier aerial inspection missions above the Arctic
Ocean performed near Greenland in summer 2017.
Generative Interest Estimation for Document Recommendations
Authors: Danijar Hafner, Alexander Immer, Willi Raschkowski, Fabian Windheuser
Date: 2017-11-28 00:00:00
Tasks: Density Estimation, Recommendation Systems

Learning distributed representations of documents has pushed the
state-of-the-art in several natural language processing tasks and was
successfully applied to the field of recommender systems recently. In this
paper, we propose a novel content-based recommender system based on learned
representations and a generative model of user interest. Our method works as
follows: First, we learn representations on a corpus of text documents. Then,
we capture a user's interest as a generative model in the space of the document
representations. In particular, we model the distribution of interest for each
user as a Gaussian mixture model (GMM). Recommendations can be obtained
directly by sampling from a user's generative model. Using Latent semantic
analysis (LSA) as comparison, we compute and explore document representations
on the Delicious bookmarks dataset, a standard benchmark for recommender
systems. We then perform density estimation in both spaces and show that
learned representations outperform LSA in terms of predictive performance.
Classification of entities via their descriptive sentences
Authors: Chao Zhao, Min Zhao, Yi Guan
Date: 2017-11-28 00:00:00
Tasks: Classification, Clustering, Descriptive, General Classification

Hypernym identification of open-domain entities is crucial for taxonomy
construction as well as many higher-level applications. Current methods suffer
from either low precision or low recall. To decrease the difficulty of this
problem, we adopt a classification-based method. We pre-define a concept
taxonomy and classify an entity to one of its leaf concept, based on the name
and description information of the entity. A convolutional neural network
classifier and a K-means clustering module are adopted for classification. We
applied this system to 2.1 million Baidu Baike entities, and 1.1 million of
them were successfully identified with a precision of 99.36%.
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning
Authors: Marc Bosch, Christopher M. Gifford, Pedro A. Rodriguez
Date: 2017-11-28 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Recent advances in Generative Adversarial Learning allow for new modalities
of image super-resolution by learning low to high resolution mappings. In this
paper we present our work using Generative Adversarial Networks (GANs) with
applications to overhead and satellite imagery. We have experimented with
several state-of-the-art architectures. We propose a GAN-based architecture
using densely connected convolutional neural networks (DenseNets) to be able to
super-resolve overhead imagery with a factor of up to 8x. We have also
investigated resolution limits of these networks. We report results on several
publicly available datasets, including SpaceNet data and IARPA Multi-View
Stereo Challenge, and compare performance with other state-of-the-art
architectures.
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
Authors: Zhaofan Qiu, Ting Yao, Tao Mei
Date: 2017-11-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Recognition, Philosophy, valid, Video Classification

Convolutional Neural Networks (CNN) have been regarded as a powerful class of
models for image recognition problems. Nevertheless, it is not trivial when
utilizing a CNN for learning spatio-temporal video representation. A few
studies have shown that performing 3D convolutions is a rewarding approach to
capture both spatial and temporal dimensions in videos. However, the
development of a very deep 3D CNN from scratch results in expensive
computational cost and memory demand. A valid question is why not recycle
off-the-shelf 2D networks for a 3D CNN. In this paper, we devise multiple
variants of bottleneck building blocks in a residual learning framework by
simulating $3\times3\times3$ convolutions with $1\times3\times3$ convolutional
filters on spatial domain (equivalent to 2D CNN) plus $3\times1\times1$
convolutions to construct temporal connections on adjacent feature maps in
time. Furthermore, we propose a new architecture, named Pseudo-3D Residual Net
(P3D ResNet), that exploits all the variants of blocks but composes each in
different placement of ResNet, following the philosophy that enhancing
structural diversity with going deep could improve the power of neural
networks. Our P3D ResNet achieves clear improvements on Sports-1M video
classification dataset against 3D CNN and frame-based 2D CNN by 5.3% and 1.8%,
respectively. We further examine the generalization performance of video
representation produced by our pre-trained P3D ResNet on five different
benchmarks and three different tasks, demonstrating superior performances over
several state-of-the-art techniques.