pwc-archive/papers-with-abstracts huggingface dataset:
Deep High Dynamic Range Imaging with Large Foreground Motions
Authors: Shangzhe Wu, Jiarui Xu, Yu-Wing Tai, Chi-Keung Tang
Date: 2017-11-24 00:00:00
Proceeding: ECCV 2018 9
Tasks: Translation, Vocal Bursts Intensity Prediction

This paper proposes the first non-flow-based deep framework for high dynamic
range (HDR) imaging of dynamic scenes with large-scale foreground motions. In
state-of-the-art deep HDR imaging, input images are first aligned using optical
flows before merging, which are still error-prone due to occlusion and large
motions. In stark contrast to flow-based methods, we formulate HDR imaging as
an image translation problem without optical flows. Moreover, our simple
translation network can automatically hallucinate plausible HDR details in the
presence of total occlusion, saturation and under-exposure, which are otherwise
almost impossible to recover by conventional optimization approaches. Our
framework can also be extended for different reference images. We performed
extensive qualitative and quantitative comparisons to show that our approach
produces excellent results where color artifacts and geometric distortions are
significantly reduced compared to existing state-of-the-art methods, and is
robust across various inputs, including images without radiometric calibration.
Articulation rate in Swedish child-directed speech increases as a function of the age of the child even when surprisal is controlled for
Authors: Johan Sjons, Thomas Hörberg, Robert Östling, Johannes Bjerva
Date: 2017-06-10 00:00:00

In earlier work, we have shown that articulation rate in Swedish
child-directed speech (CDS) increases as a function of the age of the child,
even when utterance length and differences in articulation rate between
subjects are controlled for. In this paper we show on utterance level in
spontaneous Swedish speech that i) for the youngest children, articulation rate
in CDS is lower than in adult-directed speech (ADS), ii) there is a significant
negative correlation between articulation rate and surprisal (the negative log
probability) in ADS, and iii) the increase in articulation rate in Swedish CDS
as a function of the age of the child holds, even when surprisal along with
utterance length and differences in articulation rate between speakers are
controlled for. These results indicate that adults adjust their articulation
rate to make it fit the linguistic capacity of the child.
A Gamut-Mapping Framework for Color-Accurate Reproduction of HDR Images
Authors: E. Sikudova, T. Pouli, A. Artusi, A. O. Akyuz, F. Banterle, Z. M. Mazlumoglu, E. Reinhard
Date: 2017-11-24 00:00:00
Tasks: Management, Tone Mapping

Few tone mapping operators (TMOs) take color management into consideration,
limiting compression to luminance values only. This may lead to changes in
image chroma and hues which are typically managed with a post-processing step.
However, current post-processing techniques for tone reproduction do not
explicitly consider the target display gamut. Gamut mapping on the other hand,
deals with mapping images from one color gamut to another, usually smaller,
gamut but has traditionally focused on smaller scale, chromatic changes. In
this context, we present a novel gamut and tone management framework for
color-accurate reproduction of high dynamic range (HDR) images, which is
conceptually and computationally simple, parameter-free, and compatible with
existing TMOs. In the CIE LCh color space, we compress chroma to fit the gamut
of the output color space. This prevents hue and luminance shifts while taking
gamut boundaries into consideration. We also propose a compatible lightness
compression scheme that minimizes the number of color space conversions. Our
results show that our gamut management method effectively compresses the chroma
of tone mapped images, respecting the target gamut and without reducing image
quality.
Summarizing First-Person Videos from Third Persons' Points of Views
Authors: Hsuan-I Ho, Wei-Chen Chiu, Yu-Chiang Frank Wang
Date: 2017-11-24 00:00:00
Proceeding: ECCV 2018

Video highlight or summarization is among interesting topics in computer
vision, which benefits a variety of applications like viewing, searching, or
storage. However, most existing studies rely on training data of third-person
videos, which cannot easily generalize to highlight the first-person ones. With
the goal of deriving an effective model to summarize first-person videos, we
propose a novel deep neural network architecture for describing and
discriminating vital spatiotemporal information across videos with different
points of view. Our proposed model is realized in a semi-supervised setting, in
which fully annotated third-person videos, unlabeled first-person videos, and a
small number of annotated first-person ones are presented during training. In
our experiments, qualitative and quantitative evaluations on both benchmarks
and our collected first-person video datasets are presented.
Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning
Authors: Pascal Kerschke, Heike Trautmann
Date: 2017-11-24 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we build upon previous work on designing informative and
efficient Exploratory Landscape Analysis features for characterizing problems'
landscapes and show their effectiveness in automatically constructing algorithm
selection models in continuous black-box optimization problems. Focussing on
algorithm performance results of the COCO platform of several years, we
construct a representative set of high-performing complementary solvers and
present an algorithm selection model that - compared to the portfolio's single
best solver - on average requires less than half of the resources for solving a
given problem. Therefore, there is a huge gain in efficiency compared to
classical ensemble methods combined with an increased insight into problem
characteristics and algorithm properties by using informative features. Acting
on the assumption that the function set of the Black-Box Optimization Benchmark
is representative enough for practical applications the model allows for
selecting the best suited optimization algorithm within the considered set for
unseen problems prior to the optimization itself based on a small sample of
function evaluations. Note that such a sample can even be reused for the
initial population of an evolutionary (optimization) algorithm so that even the
feature costs become negligible.
Computing the quality of the Laplace approximation
Authors: Guillaume P. Dehaene
Date: 2017-11-24 00:00:00
Tasks: Bayesian Inference

Bayesian inference requires approximation methods to become computable, but
for most of them it is impossible to quantify how close the approximation is to
the true posterior. In this work, we present a theorem upper-bounding the KL
divergence between a log-concave target density
$f\left(\boldsymbol{\theta}\right)$ and its Laplace approximation
$g\left(\boldsymbol{\theta}\right)$. The bound we present is computable: on the
classical logistic regression model, we find our bound to be almost exact as
long as the dimensionality of the parameter space is high.
  The approach we followed in this work can be extended to other Gaussian
approximations, as we will do in an extended version of this work, to be
submitted to the Annals of Statistics. It will then become a critical tool for
characterizing whether, for a given problem, a given Gaussian approximation is
suitable, or whether a more precise alternative method should be used instead.
Adversarial Transfer Learning for Cross-domain Visual Recognition
Authors: Shan-Shan Wang, Lei Zhang, JingRu Fu
Date: 2017-11-24 00:00:00
Tasks: Domain Adaptation, Transfer Learning

In many practical visual recognition scenarios, feature distribution in the source domain is generally different from that of the target domain, which results in the emergence of general cross-domain visual recognition problems. To address the problems of visual domain mismatch, we propose a novel semi-supervised adversarial transfer learning approach, which is called Coupled adversarial transfer Domain Adaptation (CatDA), for distribution alignment between two domains. The proposed CatDA approach is inspired by cycleGAN, but leveraging multiple shallow multilayer perceptrons (MLPs) instead of deep networks. Specifically, our CatDA comprises of two symmetric and slim sub-networks, such that the coupled adversarial learning framework is formulated. With such symmetry of two generators, the input data from source/target domain can be fed into the MLP network for target/source domain generation, supervised by two confrontation oriented coupled discriminators. Notably, in order to avoid the critical flaw of high-capacity of the feature extraction function during domain adversarial training, domain specific loss and domain knowledge fidelity loss are proposed in each generator, such that the effectiveness of the proposed transfer network is guaranteed. Additionally, the essential difference from cycleGAN is that our method aims to generate domain-agnostic and aligned features for domain adaptation and transfer learning rather than synthesize realistic images. We show experimentally on a number of benchmark datasets and the proposed approach achieves competitive performance over state-of-the-art domain adaptation and transfer learning approaches.
Supervised Hashing with End-to-End Binary Deep Neural Network
Authors: Dang-Khoa Le Tan, Thanh-Toan Do, Ngai-Man Cheung
Date: 2017-11-24 00:00:00
Tasks: Image Retrieval, Retrieval

Image hashing is a popular technique applied to large scale content-based
visual retrieval due to its compact and efficient binary codes. Our work
proposes a new end-to-end deep network architecture for supervised hashing
which directly learns binary codes from input images and maintains good
properties over binary codes such as similarity preservation, independence, and
balancing. Furthermore, we also propose a new learning scheme that can cope
with the binary constrained loss function. The proposed algorithm not only is
scalable for learning over large-scale datasets but also outperforms
state-of-the-art supervised hashing methods, which are illustrated throughout
extensive experiments from various image retrieval benchmarks.
Demystifying AlphaGo Zero as AlphaGo GAN
Authors: Xiao Dong, Jiasong Wu, Ling Zhou
Date: 2017-11-24 00:00:00

The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a
worldwide discussion of the future of our human society with a mixed mood of
hope, anxiousness, excitement and fear. We try to dymystify AlphaGo Zero by a
qualitative analysis to indicate that AlphaGo Zero can be understood as a
specially structured GAN system which is expected to possess an inherent good
convergence property. Thus we deduct the success of AlphaGo Zero may not be a
sign of a new generation of AI.
Computation Error Analysis of Block Floating Point Arithmetic Oriented Convolution Neural Network Accelerator Design
Authors: Zhourui Song, Zhenyu Liu, Dongsheng Wang
Date: 2017-09-22 00:00:00

The heavy burdens of computation and off-chip traffic impede deploying the
large scale convolution neural network on embedded platforms. As CNN is
attributed to the strong endurance to computation errors, employing block
floating point (BFP) arithmetics in CNN accelerators could save the hardware
cost and data traffics efficiently, while maintaining the classification
accuracy. In this paper, we verify the effects of word width definitions in BFP
to the CNN performance without retraining. Several typical CNN models,
including VGG16, ResNet-18, ResNet-50 and GoogLeNet, were tested in this paper.
Experiments revealed that 8-bit mantissa, including sign bit, in BFP
representation merely induced less than 0.3% accuracy loss. In addition, we
investigate the computational errors in theory and develop the noise-to-signal
ratio (NSR) upper bound, which provides the promising guidance for BFP based
CNN engine design.
Constrained Manifold Learning for Hyperspectral Imagery Visualization
Authors: Danping Liao, Yuntao Qian, Yuan Yan Tang
Date: 2017-11-24 00:00:00

Displaying the large number of bands in a hyper- spectral image (HSI) on a
trichromatic monitor is important for HSI processing and analysis system. The
visualized image shall convey as much information as possible from the original
HSI and meanwhile facilitate image interpretation. However, most existing
methods display HSIs in false color, which contradicts with user experience and
expectation. In this paper, we propose a visualization approach based on
constrained manifold learning, whose goal is to learn a visualized image that
not only preserves the manifold structure of the HSI but also has natural
colors. Manifold learning preserves the image structure by forcing pixels with
similar signatures to be displayed with similar colors. A composite kernel is
applied in manifold learning to incorporate both the spatial and spectral
information of HSI in the embedded space. The colors of the output image are
constrained by a corresponding natural-looking RGB image, which can either be
generated from the HSI itself (e.g., band selection from the visible
wavelength) or be captured by a separate device. Our method can be done at
instance-level and feature-level. Instance-level learning directly obtains the
RGB coordinates for the pixels in the HSI while feature-level learning learns
an explicit mapping function from the high dimensional spectral space to the
RGB space. Experimental results demonstrate the advantage of the proposed
method in information preservation and natural color visualization.
The Doctor Just Won't Accept That!
Authors: Zachary C. Lipton
Date: 2017-11-20 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning

Calls to arms to build interpretable models express a well-founded discomfort
with machine learning. Should a software agent that does not even know what a
loan is decide who qualifies for one? Indeed, we ought to be cautious about
injecting machine learning (or anything else, for that matter) into
applications where there may be a significant risk of causing social harm.
However, claims that stakeholders "just won't accept that!" do not provide a
sufficient foundation for a proposed field of study. For the field of
interpretable machine learning to advance, we must ask the following questions:
What precisely won't various stakeholders accept? What do they want? Are these
desiderata reasonable? Are they feasible? In order to answer these questions,
we'll have to give real-world problems and their respective stakeholders
greater consideration.
Feature Selective Networks for Object Detection
Authors: Yao Zhai, Jingjing Fu, Yan Lu, Houqiang Li
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, object-detection, Object Detection, Translation

Objects for detection usually have distinct characteristics in different
sub-regions and different aspect ratios. However, in prevalent two-stage object
detection methods, Region-of-Interest (RoI) features are extracted by RoI
pooling with little emphasis on these translation-variant feature components.
We present feature selective networks to reform the feature representations of
RoIs by exploiting their disparities among sub-regions and aspect ratios. Our
network produces the sub-region attention bank and aspect ratio attention bank
for the whole image. The RoI-based sub-region attention map and aspect ratio
attention map are selectively pooled from the banks, and then used to refine
the original RoI features for RoI classification. Equipped with a light-weight
detection subnetwork, our network gets a consistent boost in detection
performance based on general ConvNet backbones (ResNet-101, GoogLeNet and
VGG-16). Without bells and whistles, our detectors equipped with ResNet-101
achieve more than 3% mAP improvement compared to counterparts on PASCAL VOC
2007, PASCAL VOC 2012 and MS COCO datasets.
Continuous Semantic Topic Embedding Model Using Variational Autoencoder
Authors: Namkyu Jung, Hyeong In Choi
Date: 2017-11-24 00:00:00
Tasks: model

This paper proposes the continuous semantic topic embedding model (CSTEM)
which finds latent topic variables in documents using continuous semantic
distance function between the topics and the words by means of the variational
autoencoder(VAE). The semantic distance could be represented by any symmetric
bell-shaped geometric distance function on the Euclidean space, for which the
Mahalanobis distance is used in this paper. In order for the semantic distance
to perform more properly, we newly introduce an additional model parameter for
each word to take out the global factor from this distance indicating how
likely it occurs regardless of its topic. It certainly improves the problem
that the Gaussian distribution which is used in previous topic model with
continuous word embedding could not explain the semantic relation correctly and
helps to obtain the higher topic coherence. Through the experiments with the
dataset of 20 Newsgroup, NIPS papers and CNN/Dailymail corpus, the performance
of the recent state-of-the-art models is accomplished by our model as well as
generating topic embedding vectors which makes possible to observe where the
topic vectors are embedded with the word vectors in the real Euclidean space
and how the topics are related each other semantically.
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
Authors: Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, Yi-Hsuan Yang
Date: 2017-09-19 00:00:00
Tasks: Music Generation

Generating music has a few notable differences from generating images and
videos. First, music is an art of time, necessitating a temporal model. Second,
music is usually composed of multiple instruments/tracks with their own
temporal dynamics, but collectively they unfold over time interdependently.
Lastly, musical notes are often grouped into chords, arpeggios or melodies in
polyphonic music, and thereby introducing a chronological ordering of notes is
not naturally suitable. In this paper, we propose three models for symbolic
multi-track music generation under the framework of generative adversarial
networks (GANs). The three models, which differ in the underlying assumptions
and accordingly the network architectures, are referred to as the jamming
model, the composer model and the hybrid model. We trained the proposed models
on a dataset of over one hundred thousand bars of rock music and applied them
to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings.
A few intra-track and inter-track objective metrics are also proposed to
evaluate the generative results, in addition to a subjective user study. We
show that our models can generate coherent music of four bars right from
scratch (i.e. without human inputs). We also extend our models to human-AI
cooperative music generation: given a specific track composed by human, we can
generate four additional tracks to accompany it. All code, the dataset and the
rendered audio samples are available at https://salu133445.github.io/musegan/ .
Predicting shim gaps in aircraft assembly with machine learning and sparse sensing
Authors: Krithika Manohar, Thomas Hogan, Jim Buttrick, Ashis G. Banerjee, J. Nathan Kutz, Steven L. Brunton
Date: 2017-11-24 00:00:00
Tasks: BIG-bench Machine Learning

A modern aircraft may require on the order of thousands of custom shims to
fill gaps between structural components in the airframe that arise due to
manufacturing tolerances adding up across large structures. These shims are
necessary to eliminate gaps, maintain structural performance, and minimize
pull-down forces required to bring the aircraft into engineering nominal
configuration for peak aerodynamic efficiency. Gap filling is a time-consuming
process, involving either expensive by-hand inspection or computations on vast
quantities of measurement data from increasingly sophisticated metrology
equipment. Either case amounts to significant delays in production, with much
of the time spent in the critical path of aircraft assembly. This work presents
an alternative strategy for predictive shimming, based on machine learning and
sparse sensing to first learn gap distributions from historical data, and then
design optimized sparse sensing strategies to streamline data collection and
processing. This new approach is based on the assumption that patterns exist in
shim distributions across aircraft, which may be mined and used to reduce the
burden of data collection and processing in future aircraft. Specifically,
robust principal component analysis is used to extract low-dimensional patterns
in the gap measurements while rejecting outliers. Next, optimized sparse
sensors are obtained that are most informative about the dimensions of a new
aircraft in these low-dimensional principal components. We demonstrate the
success of the proposed approach, called PIXel Identification Despite
Uncertainty in Sensor Technology (PIXI-DUST), on historical production data
from 54 representative Boeing commercial aircraft. Our algorithm successfully
predicts $99\%$ of shim gaps within the desired measurement tolerance using
$3\%$ of the laser scan points typically required; all results are
cross-validated.
Certified Defenses for Data Poisoning Attacks
Authors: Jacob Steinhardt, Pang Wei Koh, Percy Liang
Date: 2017-06-09 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Data Poisoning

Machine learning systems trained on user-provided data are susceptible to
data poisoning attacks, whereby malicious users inject false training data with
the aim of corrupting the learned model. While recent work has proposed a
number of attacks and defenses, little is understood about the worst-case loss
of a defense in the face of a determined attacker. We address this by
constructing approximate upper bounds on the loss across a broad family of
attacks, for defenders that first perform outlier removal followed by empirical
risk minimization. Our approximation relies on two assumptions: (1) that the
dataset is large enough for statistical concentration between train and test
error to hold, and (2) that outliers within the clean (non-poisoned) data do
not have a strong effect on the model. Our bound comes paired with a candidate
attack that often nearly matches the upper bound, giving us a powerful tool for
quickly assessing defenses on a given dataset. Empirically, we find that even
under a simple defense, the MNIST-1-7 and Dogfish datasets are resilient to
attack, while in contrast the IMDB sentiment dataset can be driven from 12% to
23% test error by adding only 3% poisoned data.
Incorporating Syntactic Uncertainty in Neural Machine Translation with Forest-to-Sequence Model
Authors: Poorya Zaremoodi, Gholamreza Haffari
Date: 2017-11-19 00:00:00
Tasks: Machine Translation, Sentence, Translation

Incorporating syntactic information in Neural Machine Translation models is a
method to compensate their requirement for a large amount of parallel training
text, especially for low-resource language pairs. Previous works on using
syntactic information provided by (inevitably error-prone) parsers has been
promising. In this paper, we propose a forest-to-sequence Attentional Neural
Machine Translation model to make use of exponentially many parse trees of the
source sentence to compensate for the parser errors. Our method represents the
collection of parse trees as a packed forest, and learns a neural attentional
transduction model from the forest to the target sentence. Experiments on
English to German, Chinese and Persian translation show the superiority of our
method over the tree-to-sequence and vanilla sequence-to-sequence neural
translation models.