pwc-archive/papers-with-abstracts huggingface dataset:
Actor-Critic based Training Framework for Abstractive Summarization
Authors: Piji Li, Lidong Bing, Wai Lam
Date: 2018-03-28 00:00:00
Tasks: Abstractive Text Summarization, Reinforcement Learning

We present a training framework for neural abstractive summarization based on
actor-critic approaches from reinforcement learning. In the traditional neural
network based methods, the objective is only to maximize the likelihood of the
predicted summaries, no other assessment constraints are considered, which may
generate low-quality summaries or even incorrect sentences. To alleviate this
problem, we employ an actor-critic framework to enhance the training procedure.
For the actor, we employ the typical attention based sequence-to-sequence
(seq2seq) framework as the policy network for summary generation. For the
critic, we combine the maximum likelihood estimator with a well designed global
summary quality estimator which is a neural network based binary classifier
aiming to make the generated summaries indistinguishable from the human-written
ones. Policy gradient method is used to conduct the parameter learning. An
alternating training strategy is proposed to conduct the joint training of the
actor and critic models. Extensive experiments on some benchmark datasets in
different languages show that our framework achieves improvements over the
state-of-the-art methods.
DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images
Authors: Taihong Xiao, Jiapeng Hong, Jinwen Ma
Date: 2017-11-15 00:00:00
Proceeding: ICLR 2018 1
Tasks: Attribute, Representation Learning

Disentangling factors of variation has become a very challenging problem on
representation learning. Existing algorithms suffer from many limitations, such
as unpredictable disentangling factors, poor quality of generated images from
encodings, lack of identity information, etc. In this paper, we propose a
supervised learning model called DNA-GAN which tries to disentangle different
factors or attributes of images. The latent representations of images are
DNA-like, in which each individual piece (of the encoding) represents an
independent factor of the variation. By annihilating the recessive piece and
swapping a certain piece of one latent representation with that of the other
one, we obtain two different representations which could be decoded into two
kinds of images with the existence of the corresponding attribute being
changed. In order to obtain realistic images and also disentangled
representations, we further introduce the discriminator for adversarial
training. Experiments on Multi-PIE and CelebA datasets finally demonstrate that
our proposed method is effective for factors disentangling and even overcome
certain limitations of the existing methods.
Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart
Authors: Chao Yang, Yuhang Song, Xiaofeng Liu, Qingming Tang, C. -C. Jay Kuo
Date: 2018-03-23 00:00:00
Tasks: Facial Inpainting, Image Harmonization, Image Inpainting

Recent advances in deep generative models have shown promising potential in
image inpanting, which refers to the task of predicting missing pixel values of
an incomplete image using the known context. However, existing methods can be
slow or generate unsatisfying results with easily detectable flaws. In
addition, there is often perceivable discontinuity near the holes and require
further post-processing to blend the results. We present a new approach to
address the difficulty of training a very deep generative model to synthesize
high-quality photo-realistic inpainting. Our model uses conditional generative
adversarial networks (conditional GANs) as the backbone, and we introduce a
novel block-wise procedural training scheme to stabilize the training while we
increase the network depth. We also propose a new strategy called adversarial
loss annealing to reduce the artifacts. We further describe several losses
specifically designed for inpainting and show their effectiveness. Extensive
experiments and user-study show that our approach outperforms existing methods
in several tasks such as inpainting, face completion and image harmonization.
Finally, we show our framework can be easily used as a tool for interactive
guided inpainting, demonstrating its practical value to solve common real-world
challenges.
Automatic Stroke Lesions Segmentation in Diffusion-Weighted MRI
Authors: Noranart Vesdapunt, Nongluk Covavisaruch
Date: 2018-03-28 00:00:00
Tasks: Segmentation, Specificity

Diffusion-Weighted Magnetic Resonance Imaging (DWI) is widely used for early
cerebral infarct detection caused by ischemic stroke. Manual segmentation is
done by a radiologist as a common clinical process, nonetheless, challenges of
cerebral infarct segmentation come from low resolution and uncertain
boundaries. Many segmentation techniques have been proposed and proved by
manual segmentation as gold standard. In order to reduce human error in
research operation and clinical process, we adopt a semi-automatic segmentation
as gold standard using Fluid-Attenuated Inversion-Recovery (FLAIR) Magnetic
Resonance Image (MRI) from the same patient under controlled environment.
Extensive testing is performed on popular segmentation algorithms including
Otsu method, Fuzzy C-means, Hill-climbing based segmentation, and Growcut. The
selected segmentation techniques have been validated by accuracy, sensitivity,
and specificity using leave-one-out cross-validation to determine the
possibility of each techniques first then maximizes the accuracy from the
training set. Our experimental results demonstrate the effectiveness of
selected methods.
Topic Modeling Based Multi-modal Depression Detection
Authors: Yuan Gong, Christian Poellabauer
Date: 2018-03-28 00:00:00
Tasks: Depression Detection

Major depressive disorder is a common mental disorder that affects almost 7%
of the adult U.S. population. The 2017 Audio/Visual Emotion Challenge (AVEC)
asks participants to build a model to predict depression levels based on the
audio, video, and text of an interview ranging between 7-33 minutes. Since
averaging features over the entire interview will lose most temporal
information, how to discover, capture, and preserve useful temporal details for
such a long interview are significant challenges. Therefore, we propose a novel
topic modeling based approach to perform context-aware analysis of the
recording. Our experiments show that the proposed approach outperforms
context-unaware methods and the challenge baselines for all metrics.
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
Authors: Angela Dai, Daniel Ritchie, Martin Bokeloh, Scott Reed, Jürgen Sturm, Matthias Nießner
Date: 2017-12-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Semantic Segmentation

We introduce ScanComplete, a novel data-driven approach for taking an
incomplete 3D scan of a scene as input and predicting a complete 3D model along
with per-voxel semantic labels. The key contribution of our method is its
ability to handle large scenes with varying spatial extent, managing the cubic
growth in data size as scene size increases. To this end, we devise a
fully-convolutional generative 3D CNN model whose filter kernels are invariant
to the overall scene size. The model can be trained on scene subvolumes but
deployed on arbitrarily large scenes at test time. In addition, we propose a
coarse-to-fine inference strategy in order to produce high-resolution output
while also leveraging large input context sizes. In an extensive series of
experiments, we carefully evaluate different model design choices, considering
both deterministic and probabilistic models for completion and semantic
inference. Our results show that we outperform other methods not only in the
size of the environments handled and processing efficiency, but also with
regard to completion quality and semantic segmentation performance by a
significant margin.
Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement
Authors: Faris B. Mismar, Brian L. Evans
Date: 2017-07-10 00:00:00
Tasks: Deep Reinforcement Learning, Management, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose an algorithm to automate fault management in an outdoor cellular
network using deep reinforcement learning (RL) against wireless impairments.
This algorithm enables the cellular network cluster to self-heal by allowing RL
to learn how to improve the downlink signal to interference plus noise ratio
through exploration and exploitation of various alarm corrective actions. The
main contributions of this paper are to 1) introduce a deep RL-based fault
handling algorithm which self-organizing networks can implement in a polynomial
runtime and 2) show that this fault management method can improve the radio
link performance in a realistic network setup. Simulation results show that our
proposed algorithm learns an action sequence to clear alarms and improve the
performance in the cellular cluster better than existing algorithms, even
against the randomness of the network fault occurrences and user movements.
A Framework for Evaluating 6-DOF Object Trackers
Authors: Mathieu Garon, Denis Laurendeau, Jean-François Lalonde
Date: 2018-03-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, Object Tracking

We present a challenging and realistic novel dataset for evaluating 6-DOF
object tracking algorithms. Existing datasets show serious
limitations---notably, unrealistic synthetic data, or real data with large
fiducial markers---preventing the community from obtaining an accurate picture
of the state-of-the-art. Using a data acquisition pipeline based on a
commercial motion capture system for acquiring accurate ground truth poses of
real objects with respect to a Kinect V2 camera, we build a dataset which
contains a total of 297 calibrated sequences. They are acquired in three
different scenarios to evaluate the performance of trackers: stability,
robustness to occlusion and accuracy during challenging interactions between a
person and the object. We conduct an extensive study of a deep 6-DOF tracking
architecture and determine a set of optimal parameters. We enhance the
architecture and the training methodology to train a 6-DOF tracker that can
robustly generalize to objects never seen during training, and demonstrate
favorable performance compared to previous approaches trained specifically on
the objects to track.
Space-efficient Feature Maps for String Alignment Kernels
Authors: Yasuo Tabei, Yoshihiro Yamanishi, Rasmus Pagh
Date: 2018-02-18 00:00:00

String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVM in various applications. However, alignment kernels have a crucial drawback in that they scale poorly due to their quadratic computation complexity in the number of input strings, which limits large-scale applications in practice. We address this need by presenting the first approximation for string alignment kernels, which we call space-efficient feature maps for edit distance with moves (SFMEDM), by leveraging a metric embedding named edit sensitive parsing (ESP) and feature maps (FMs) of random Fourier features (RFFs) for large-scale string analyses. The original FMs for RFFs consume a huge amount of memory proportional to the dimension d of input vectors and the dimension D of output vectors, which prohibits its large-scale applications. We present novel space-efficient feature maps (SFMs) of RFFs for a space reduction from O(dD) of the original FMs to O(d) of SFMs with a theoretical guarantee with respect to concentration bounds. We experimentally test SFMEDM on its ability to learn SVM for large-scale string classifications with various massive string data, and we demonstrate the superior performance of SFMEDM with respect to prediction accuracy, scalability and computation efficiency.
On the Algorithmic Power of Spiking Neural Networks
Authors: Chi-Ning Chou, Kai-Min Chung, Chi-Jen Lu
Date: 2018-03-28 00:00:00

Spiking Neural Networks (SNN) are mathematical models in neuroscience to
describe the dynamics among a set of neurons that interact with each other by
firing instantaneous signals, a.k.a., spikes. Interestingly, a recent advance
in neuroscience [Barrett-Den\`eve-Machens, NIPS 2013] showed that the neurons'
firing rate, i.e., the average number of spikes fired per unit of time, can be
characterized by the optimal solution of a quadratic program defined by the
parameters of the dynamics. This indicated that SNN potentially has the
computational power to solve non-trivial quadratic programs. However, the
results were justified empirically without rigorous analysis.
  We put this into the context of natural algorithms and aim to investigate the
algorithmic power of SNN. Especially, we emphasize on giving rigorous
asymptotic analysis on the performance of SNN in solving optimization problems.
To enforce a theoretical study, we first identify a simplified SNN model that
is tractable for analysis. Next, we confirm the empirical observation in the
work of Barrett et al. by giving an upper bound on the convergence rate of SNN
in solving the quadratic program. Further, we observe that in the case where
there are infinitely many optimal solutions, SNN tends to converge to the one
with smaller l1 norm. We give an affirmative answer to our finding by showing
that SNN can solve the l1 minimization problem under some regular conditions.
  Our main technical insight is a dual view of the SNN dynamics, under which
SNN can be viewed as a new natural primal-dual algorithm for the l1
minimization problem. We believe that the dual view is of independent interest
and may potentially find interesting interpretation in neuroscience.
Future Person Localization in First-Person Videos
Authors: Takuma Yagi, Karttikeya Mangalam, Ryo Yonetani, Yoichi Sato
Date: 2017-11-30 00:00:00
Proceeding: CVPR 2018 6

We present a new task that predicts future locations of people observed in
first-person videos. Consider a first-person video stream continuously recorded
by a wearable camera. Given a short clip of a person that is extracted from the
complete stream, we aim to predict that person's location in future frames. To
facilitate this future person localization ability, we make the following three
key observations: a) First-person videos typically involve significant
ego-motion which greatly affects the location of the target person in future
frames; b) Scales of the target person act as a salient cue to estimate a
perspective effect in first-person videos; c) First-person videos often capture
people up-close, making it easier to leverage target poses (e.g., where they
look) for predicting their future locations. We incorporate these three
observations into a prediction framework with a multi-stream
convolution-deconvolution architecture. Experimental results reveal our method
to be effective on our new dataset as well as on a public social interaction
dataset.
Cross-lingual Distillation for Text Classification
Authors: Ruochen Xu, Yiming Yang
Date: 2017-05-05 00:00:00
Proceeding: ACL 2017 7
Tasks: Classification, General Classification, Model Compression, text-classification, Text Classification

Cross-lingual text classification(CLTC) is the task of classifying documents
written in different languages into the same taxonomy of categories. This paper
presents a novel approach to CLTC that builds on model distillation, which
adapts and extends a framework originally proposed for model compression. Using
soft probabilistic predictions for the documents in a label-rich language as
the (induced) supervisory labels in a parallel corpus of documents, we train
classifiers successfully for new languages in which labeled training data are
not available. An adversarial feature adaptation technique is also applied
during the model training to reduce distribution mismatch. We conducted
experiments on two benchmark CLTC datasets, treating English as the source
language and German, French, Japan and Chinese as the unlabeled target
languages. The proposed approach had the advantageous or comparable performance
of the other state-of-art methods.
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system
Authors: Kendall Lowrey, Svetoslav Kolev, Jeremy Dao, Aravind Rajeswaran, Emanuel Todorov
Date: 2018-03-28 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning has emerged as a promising methodology for training
robot controllers. However, most results have been limited to simulation due to
the need for a large number of samples and the lack of automated-yet-safe data
collection methods. Model-based reinforcement learning methods provide an
avenue to circumvent these challenges, but the traditional concern has been the
mismatch between the simulator and the real world. Here, we show that control
policies learned in simulation can successfully transfer to a physical system,
composed of three Phantom robots pushing an object to various desired target
positions. We use a modified form of the natural policy gradient algorithm for
learning, applied to a carefully identified simulation model. The resulting
policies, trained entirely in simulation, work well on the physical system
without additional training. In addition, we show that training with an
ensemble of models makes the learned policies more robust to modeling errors,
thus compensating for difficulties in system identification.
Smoothed Online Convex Optimization in High Dimensions via Online Balanced Descent
Authors: Niangjun Chen, Gautam Goel, Adam Wierman
Date: 2018-03-28 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We study Smoothed Online Convex Optimization, a version of online convex
optimization where the learner incurs a penalty for changing her actions
between rounds. Given a $\Omega(\sqrt{d})$ lower bound on the competitive ratio
of any online algorithm, where $d$ is the dimension of the action space, we ask
under what conditions this bound can be beaten. We introduce a novel
algorithmic framework for this problem, Online Balanced Descent (OBD), which
works by iteratively projecting the previous point onto a carefully chosen
level set of the current cost function so as to balance the switching costs and
hitting costs. We demonstrate the generality of the OBD framework by showing
how, with different choices of "balance," OBD can improve upon state-of-the-art
performance guarantees for both competitive ratio and regret, in particular,
OBD is the first algorithm to achieve a dimension-free competitive ratio, $3 +
O(1/\alpha)$, for locally polyhedral costs, where $\alpha$ measures the
"steepness" of the costs. We also prove bounds on the dynamic regret of OBD
when the balance is performed in the dual space that are dimension-free and
imply that OBD has sublinear static regret.
ClickBAIT-v2: Training an Object Detector in Real-Time
Authors: Ervin Teng, Rui Huang, Bob Iannucci
Date: 2018-03-27 00:00:00
Tasks: image-classification, Image Classification, Interactive Segmentation, Object, object-detection, Object Detection, Object Tracking

Modern deep convolutional neural networks (CNNs) for image classification and
object detection are often trained offline on large static datasets. Some
applications, however, will require training in real-time on live video streams
with a human-in-the-loop. We refer to this class of problem as time-ordered
online training (ToOT). These problems will require a consideration of not only
the quantity of incoming training data, but the human effort required to
annotate and use it. We demonstrate and evaluate a system tailored to training
an object detector on a live video stream with minimal input from a human
operator. We show that we can obtain bounding box annotation from
weakly-supervised single-point clicks through interactive segmentation.
Furthermore, by exploiting the time-ordered nature of the video stream through
object tracking, we can increase the average training benefit of human
interactions by 3-4 times.
Structural inpainting
Authors: Huy V. Vo, Ngoc Q. K. Duong, Patrick Perez
Date: 2018-03-27 00:00:00

Scene-agnostic visual inpainting remains very challenging despite progress in
patch-based methods. Recently, Pathak et al. 2016 have introduced convolutional
"context encoders" (CEs) for unsupervised feature learning through image
completion tasks. With the additional help of adversarial training, CEs turned
out to be a promising tool to complete complex structures in real inpainting
problems. In the present paper we propose to push further this key ability by
relying on perceptual reconstruction losses at training time. We show on a wide
variety of visual scenes the merit of the approach for structural inpainting,
and confirm it through a user study. Combined with the optimization-based
refinement of Yang et al. 2016 with neural patches, our context encoder opens
up new opportunities for prior-free visual inpainting.
Bayesian Incremental Learning for Deep Neural Networks
Authors: Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov
Date: 2018-02-20 00:00:00
Tasks: Incremental Learning

In industrial machine learning pipelines, data often arrive in parts.
Particularly in the case of deep neural networks, it may be too expensive to
train the model from scratch each time, so one would rather use a previously
learned model and the new data to improve performance. However, deep neural
networks are prone to getting stuck in a suboptimal solution when trained on
only new data as compared to the full dataset. Our work focuses on a continuous
learning setup where the task is always the same and new parts of data arrive
sequentially. We apply a Bayesian approach to update the posterior
approximation with each new piece of data and find this method to outperform
the traditional approach in our experiments.
GAGAN: Geometry-Aware Generative Adversarial Networks
Authors: Jean Kossaifi, Linh Tran, Yannis Panagakis, Maja Pantic
Date: 2017-12-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Generation, Image Generation

Deep generative models learned through adversarial training have become
increasingly popular for their ability to generate naturalistic image textures.
However, aside from their texture, the visual appearance of objects is
significantly influenced by their shape geometry; information which is not
taken into account by existing generative models. This paper introduces the
Geometry-Aware Generative Adversarial Networks (GAGAN) for incorporating
geometric information into the image generation process. Specifically, in GAGAN
the generator samples latent variables from the probability space of a
statistical shape model. By mapping the output of the generator to a canonical
coordinate frame through a differentiable geometric transformation, we enforce
the geometry of the objects and add an implicit connection from the prior to
the generated object. Experimental results on face generation indicate that the
GAGAN can generate realistic images of faces with arbitrary facial attributes
such as facial expression, pose, and morphology, that are of better quality
than current GAN-based methods. Our method can be used to augment any existing
GAN architecture and improve the quality of the images generated.