pwc-archive/papers-with-abstracts huggingface dataset:
Learning a Disentangled Embedding for Monocular 3D Shape Retrieval and Pose Estimation
Authors: Kyaw Zaw Lin, Weipeng Xu, Qianru Sun, Christian Theobalt, Tat-Seng Chua
Date: 2018-12-24 00:00:00
Tasks: 3D Object Retrieval, 3D Shape Classification, 3D Shape Retrieval, Pose Estimation, Retrieval

We propose a novel approach to jointly perform 3D shape retrieval and pose
estimation from monocular images.In order to make the method robust to
real-world image variations, e.g. complex textures and backgrounds, we learn an
embedding space from 3D data that only includes the relevant information,
namely the shape and pose. Our approach explicitly disentangles a shape vector
and a pose vector, which alleviates both pose bias for 3D shape retrieval and
categorical bias for pose estimation. We then train a CNN to map the images to
this embedding space, and then retrieve the closest 3D shape from the database
and estimate the 6D pose of the object. Our method achieves 10.3 median error
for pose estimation and 0.592 top-1-accuracy for category agnostic 3D object
retrieval on the Pascal3D+ dataset, outperforming the previous state-of-the-art
methods on both tasks.
A Bayesian Model for Bivariate Causal Inference
Authors: Maximilian Kurthen, Torsten A. Enßlin
Date: 2018-12-24 00:00:00
Tasks: Benchmarking, Causal Inference, model, Model Selection, Relation

We address the problem of two-variable causal inference without intervention. This task is to infer an existing causal relation between two random variables, i.e. $X \rightarrow Y$ or $Y \rightarrow X$ , from purely observational data. As the option to modify a potential cause is not given in many situations only structural properties of the data can be used to solve this ill-posed problem. We briefly review a number of state-of-the-art methods for this, including very recent ones. A novel inference method is introduced, Bayesian Causal Inference (BCI), which assumes a generative Bayesian hierarchical model to pursue the strategy of Bayesian model selection. In the adopted model the distribution of the cause variable is given by a Poisson lognormal distribution, which allows to explicitly regard the discrete nature of datasets, correlations in the parameter spaces, as well as the variance of probability densities on logarithmic scales. We assume Fourier diagonal Field covariance operators. The model itself is restricted to use cases where a direct causal relation $X \rightarrow Y$ has to be decided against a relation $Y \rightarrow X$ , therefore we compare it other methods for this exact problem setting. The generative model assumed provides synthetic causal data for benchmarking our model in comparison to existing State-of-the-art models, namely LiNGAM , ANM-HSIC , ANM-MML , IGCI and CGNN . We explore how well the above methods perform in case of high noise settings, strongly discretized data and very sparse data. BCI performs generally reliable with synthetic data as well as with the real world TCEP benchmark set, with an accuracy comparable to state-of-the-art algorithms. We discuss directions for the future development of BCI .
Motion Blur removal via Coupled Autoencoder
Authors: Kavya Gupta, Brojeshwar Bhowmick, Angshul Majumdar
Date: 2018-12-24 00:00:00
Tasks: Deblurring, Transfer Learning

In this paper a joint optimization technique has been proposed for coupled
autoencoder which learns the autoencoder weights and coupling map (between
source and target) simultaneously. The technique is applicable to any transfer
learning problem. In this work, we propose a new formulation that recasts
deblurring as a transfer learning problem, it is solved using the proposed
coupled autoencoder. The proposed technique can operate on-the-fly, since it
does not require solving any costly inverse problem. Experiments have been
carried out on state-of-the-art techniques, our method yields better quality
images in shorter operating times.
Coupled Analysis Dictionary Learning to inductively learn inversion: Application to real-time reconstruction of Biomedical signals
Authors: Kavya Gupta, Brojeshwar Bhowmick, Angshul Majumdar
Date: 2018-12-24 00:00:00
Tasks: compressed sensing, Denoising, Dictionary Learning, Inductive Learning

This work addresses the problem of reconstructing biomedical signals from
their lower dimensional projections. Traditionally Compressed Sensing (CS)
based techniques have been employed for this task. These are transductive
inversion processes, the problem with these approaches is that the inversion is
time-consuming and hence not suitable for real-time applications. With the
recent advent of deep learning, Stacked Sparse Denoising Autoencoder (SSDAE)
has been used for learning inversion in an inductive setup. The training period
for inductive learning is large but is very fast during application -- capable
of real-time speed. This work proposes a new approach for inductive learning of
the inversion process. It is based on Coupled Analysis Dictionary Learning.
Results on Biomedical signal reconstruction show that our proposed approach is
very fast and yields result far better than CS and SSDAE.
Latent Filter Scaling for Multimodal Unsupervised Image-to-Image Translation
Authors: Yazeed Alharbi, Neil Smith, Peter Wonka
Date: 2018-12-24 00:00:00
Proceeding: CVPR 2019 6
Tasks: Disentanglement, Diversity, Generative Adversarial Network, Image-to-Image Translation, Multimodal Unsupervised Image-To-Image Translation, Translation, Unsupervised Image-To-Image Translation

In multimodal unsupervised image-to-image translation tasks, the goal is to
translate an image from the source domain to many images in the target domain.
We present a simple method that produces higher quality images than current
state-of-the-art while maintaining the same amount of multimodal diversity.
Previous methods follow the unconditional approach of trying to map the latent
code directly to a full-size image. This leads to complicated network
architectures with several introduced hyperparameters to tune. By treating the
latent code as a modifier of the convolutional filters, we produce multimodal
output while maintaining the traditional Generative Adversarial Network (GAN)
loss and without additional hyperparameters. The only tuning required by our
method controls the tradeoff between variability and quality of generated
images. Furthermore, we achieve disentanglement between source domain content
and target domain style for free as a by-product of our formulation. We perform
qualitative and quantitative experiments showing the advantages of our method
compared with the state-of-the art on multiple benchmark image-to-image
translation datasets.
Perceptual deep depth super-resolution
Authors: Oleg Voynov, Alexey Artemov, Vage Egiazarian, Alexander Notchenko, Gleb Bobrovskikh, Denis Zorin, Evgeny Burnaev
Date: 2018-12-24 00:00:00
Proceeding: ICCV 2019 10
Tasks: Super-Resolution

RGBD images, combining high-resolution color and lower-resolution depth from various types of depth sensors, are increasingly common. One can significantly improve the resolution of depth maps by taking advantage of color information; deep learning methods make combining color and depth information particularly easy. However, fusing these two sources of data may lead to a variety of artifacts. If depth maps are used to reconstruct 3D shapes, e.g., for virtual reality applications, the visual quality of upsampled images is particularly important. The main idea of our approach is to measure the quality of depth map upsampling using renderings of resulting 3D surfaces. We demonstrate that a simple visual appearance-based loss, when used with either a trained CNN or simply a deep prior, yields significantly improved 3D shapes, as measured by a number of existing perceptual metrics. We compare this approach with a number of existing optimization and learning-based techniques.
bigMap: Big Data Mapping with Parallelized t-SNE
Authors: Joan Garriga, Frederic Bartumeus
Date: 2018-12-24 00:00:00
Tasks: Clustering, Density Estimation, Dimensionality Reduction

We introduce an improved unsupervised clustering protocol specially suited for large-scale structured data. The protocol follows three steps: a dimensionality reduction of the data, a density estimation over the low dimensional representation of the data, and a final segmentation of the density landscape. For the dimensionality reduction step we introduce a parallelized implementation of the well-known t-Stochastic Neighbouring Embedding (t-SNE) algorithm that significantly alleviates some inherent limitations, while improving its suitability for large datasets. We also introduce a new adaptive Kernel Density Estimation particularly coupled with the t-SNE framework in order to get accurate density estimates out of the embedded data, and a variant of the rainfalling watershed algorithm to identify clusters within the density landscape. The whole mapping protocol is wrapped in the bigMap R package, together with visualization and analysis tools to ease the qualitative and quantitative assessment of the clustering.
Generalization Bounds for Uniformly Stable Algorithms
Authors: Vitaly Feldman, Jan Vondrak
Date: 2018-12-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Generalization Bounds

Uniform stability of a learning algorithm is a classical notion of
algorithmic stability introduced to derive high-probability bounds on the
generalization error (Bousquet and Elisseeff, 2002). Specifically, for a loss
function with range bounded in $[0,1]$, the generalization error of a
$\gamma$-uniformly stable learning algorithm on $n$ samples is known to be
within $O((\gamma +1/n) \sqrt{n \log(1/\delta)})$ of the empirical error with
probability at least $1-\delta$. Unfortunately, this bound does not lead to
meaningful generalization bounds in many common settings where $\gamma \geq
1/\sqrt{n}$. At the same time the bound is known to be tight only when $\gamma
= O(1/n)$.
  We substantially improve generalization bounds for uniformly stable
algorithms without making any additional assumptions. First, we show that the
bound in this setting is $O(\sqrt{(\gamma + 1/n) \log(1/\delta)})$ with
probability at least $1-\delta$. In addition, we prove a tight bound of
$O(\gamma^2 + 1/n)$ on the second moment of the estimation error. The best
previous bound on the second moment is $O(\gamma + 1/n)$. Our proofs are based
on new analysis techniques and our results imply substantially stronger
generalization guarantees for several well-studied algorithms.
Moment Matching Training for Neural Machine Translation: A Preliminary Study
Authors: Cong Duy Vu Hoang, Ioan Calapodescu, Marc Dymetman
Date: 2018-12-24 00:00:00
Tasks: Machine Translation, Reinforcement Learning, Translation

In previous works, neural sequence models have been shown to improve
significantly if external prior knowledge can be provided, for instance by
allowing the model to access the embeddings of explicit features during both
training and inference. In this work, we propose a different point of view on
how to incorporate prior knowledge in a principled way, using a moment matching
framework. In this approach, the standard local cross-entropy training of the
sequential model is combined with a moment matching training mode that
encourages the equality of the expectations of certain predefined features
between the model distribution and the empirical distribution. In particular,
we show how to derive unbiased estimates of some stochastic gradients that are
central to the training, and compare our framework with a formally related one:
policy gradient training in reinforcement learning, pointing out some important
differences in terms of the kinds of prior assumptions in both approaches. Our
initial results are promising, showing the effectiveness of our proposed
framework.
BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulation
Authors: Tommy Hosman, Marco Vilela, Daniel Milstein, Jessica N. Kelemen, David M. Brandman, Leigh R. Hochberg, John D. Simeral
Date: 2018-12-24 00:00:00
Tasks: Decoder

Intracortical brain computer interfaces (iBCIs) using linear Kalman decoders
have enabled individuals with paralysis to control a computer cursor for
continuous point-and-click typing on a virtual keyboard, browsing the internet,
and using familiar tablet apps. However, further advances are needed to deliver
iBCI-enabled cursor control approaching able-bodied performance. Motivated by
recent evidence that nonlinear recurrent neural networks (RNNs) can provide
higher performance iBCI cursor control in nonhuman primates (NHPs), we
evaluated decoding of intended cursor velocity from human motor cortical
signals using a long-short term memory (LSTM) RNN trained across multiple days
of multi-electrode recordings. Running simulations with previously recorded
intracortical signals from three BrainGate iBCI trial participants, we
demonstrate an RNN that can substantially increase bits-per-second metric in a
high-speed cursor-based target selection task as well as a challenging
small-target high-accuracy task when compared to a Kalman decoder. These
results indicate that RNN decoding applied to human intracortical signals could
achieve substantial performance advances in continuous 2-D cursor control and
motivate a real-time RNN implementation for online evaluation by individuals
with tetraplegia.
Holistic Decomposition Convolution for Effective Semantic Segmentation of 3D MR Images
Authors: Guodong Zeng, Guoyan Zheng
Date: 2018-12-24 00:00:00
Tasks: GPU, Image Segmentation, Medical Image Analysis, Semantic Segmentation

Convolutional Neural Networks (CNNs) have achieved state-of-the-art
performance in many different 2D medical image analysis tasks. In clinical
practice, however, a large part of the medical imaging data available is in 3D.
This has motivated the development of 3D CNNs for volumetric image segmentation
in order to benefit from more spatial context. Due to GPU memory restrictions
caused by moving to fully 3D, state-of-the-art methods depend on
subvolume/patch processing and the size of the input patch is usually small,
limiting the incorporation of larger context information for a better
performance. In this paper, we propose a novel Holistic Decomposition
Convolution (HDC), for an effective and efficient semantic segmentation of
volumetric images. HDC consists of a periodic down-shuffling operation followed
by a conventional 3D convolution. HDC has the advantage of significantly
reducing the size of the data for sub-sequential processing while using all the
information available in the input irrespective of the down-shuffling factors.
Results obtained from comprehensive experiments conducted on hip T1 MR images
and intervertebral disc T2 MR images demonstrate the efficacy of the present
approach.
Texture Deformation Based Generative Adversarial Networks for Face Editing
Authors: WenTing Chen, Xinpeng Xie, Xi Jia, Linlin Shen
Date: 2018-12-24 00:00:00
Tasks: Attribute, Image-to-Image Translation, Translation

Despite the significant success in image-to-image translation and latent
representation based facial attribute editing and expression synthesis, the
existing approaches still have limitations in the sharpness of details,
distinct image translation and identity preservation. To address these issues,
we propose a Texture Deformation Based GAN, namely TDB-GAN, to disentangle
texture from original image and transfers domains based on the extracted
texture. The approach utilizes the texture to transfer facial attributes and
expressions without the consideration of the object pose. This leads to shaper
details and more distinct visual effect of the synthesized faces. In addition,
it brings the faster convergence during training. The effectiveness of the
proposed method is validated through extensive ablation studies. We also
evaluate our approach qualitatively and quantitatively on facial attribute and
facial expression synthesis. The results on both the CelebA and RaFD datasets
suggest that Texture Deformation Based GAN achieves better performance.
Precision Highway for Ultra Low-Precision Quantization
Authors: Eunhyeok Park, Dongyoung Kim, Sungjoo Yoo, Peter Vajda
Date: 2018-12-24 00:00:00
Proceeding: ICLR 2019 5
Tasks: Language Modeling, Language Modelling, Quantization

Neural network quantization has an inherent problem called accumulated
quantization error, which is the key obstacle towards ultra-low precision,
e.g., 2- or 3-bit precision. To resolve this problem, we propose precision
highway, which forms an end-to-end high-precision information flow while
performing the ultra low-precision computation. First, we describe how the
precision highway reduce the accumulated quantization error in both
convolutional and recurrent neural networks. We also provide the quantitative
analysis of the benefit of precision highway and evaluate the overhead on the
state-of-the-art hardware accelerator. In the experiments, our proposed method
outperforms the best existing quantization methods while offering 3-bit
weight/activation quantization with no accuracy loss and 2-bit quantization
with a 2.45 % top-1 accuracy loss in ResNet-50. We also report that the
proposed method significantly outperforms the existing method in the 2-bit
quantization of an LSTM for language modeling.
Writer-Aware CNN for Parsimonious HMM-Based Offline Handwritten Chinese Text Recognition
Authors: Zi-Rui Wang, Jun Du, Jia-Ming Wang
Date: 2018-12-24 00:00:00
Tasks: Handwritten Chinese Text Recognition, Language Modeling, Language Modelling

Recently, the hybrid convolutional neural network hidden Markov model (CNN-HMM) has been introduced for offline handwritten Chinese text recognition (HCTR) and has achieved state-of-the-art performance. However, modeling each of the large vocabulary of Chinese characters with a uniform and fixed number of hidden states requires high memory and computational costs and makes the tens of thousands of HMM state classes confusing. Another key issue of CNN-HMM for HCTR is the diversified writing style, which leads to model strain and a significant performance decline for specific writers. To address these issues, we propose a writer-aware CNN based on parsimonious HMM (WCNN-PHMM). First, PHMM is designed using a data-driven state-tying algorithm to greatly reduce the total number of HMM states, which not only yields a compact CNN by state sharing of the same or similar radicals among different Chinese characters but also improves the recognition accuracy due to the more accurate modeling of tied states and the lower confusion among them. Second, WCNN integrates each convolutional layer with one adaptive layer fed by a writer-dependent vector, namely, the writer code, to extract the irrelevant variability in writer information to improve recognition performance. The parameters of writer-adaptive layers are jointly optimized with other network parameters in the training stage, while a multiple-pass decoding strategy is adopted to learn the writer code and generate recognition results. Validated on the ICDAR 2013 competition of CASIA-HWDB database, the more compact WCNN-PHMM of a 7360-class vocabulary can achieve a relative character error rate (CER) reduction of 16.6% over the conventional CNN-HMM without considering language modeling. By adopting a powerful hybrid language model (N-gram language model and recurrent neural network language model), the CER of WCNN-PHMM is reduced to 3.17%.
Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks
Authors: Thomas Brunner, Frederik Diehl, Michael Truong Le, Alois Knoll
Date: 2018-12-24 00:00:00
Proceeding: ICCV 2019 10
Tasks: image-classification, Image Classification

We consider adversarial examples for image classification in the black-box decision-based setting. Here, an attacker cannot access confidence scores, but only the final label. Most attacks for this scenario are either unreliable or inefficient. Focusing on the latter, we show that a specific class of attacks, Boundary Attacks, can be reinterpreted as a biased sampling framework that gains efficiency from domain knowledge. We identify three such biases, image frequency, regional masks and surrogate gradients, and evaluate their performance against an ImageNet classifier. We show that the combination of these biases outperforms the state of the art by a wide margin. We also showcase an efficient way to attack the Google Cloud Vision API, where we craft convincing perturbations with just a few hundred queries. Finally, the methods we propose have also been found to work very well against strong defenses: Our targeted attack won second place in the NeurIPS 2018 Adversarial Vision Challenge.
Pansori: ASR Corpus Generation from Open Online Video Contents
Authors: Yoona Choi, Bowon Lee
Date: 2018-12-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper introduces Pansori, a program used to create ASR (automatic speech
recognition) corpora from online video contents. It utilizes a cloud-based
speech API to easily create a corpus in different languages. Using this
program, we semi-automatically generated the Pansori-TEDxKR dataset from Korean
TED conference talks with community-transcribed subtitles. It is the first
high-quality corpus for the Korean language freely available for independent
research. Pansori is released as an open-source software and the generated
corpus is released under a permissive public license for community use and
participation.
Deep Learning for Inferring the Surface Solar Irradiance from Sky Imagery
Authors: Mehdi Zakroum, Mounir Ghogho, Mustapha Faqir, Mohamed Aymane Ahajjam
Date: 2018-12-23 00:00:00
Tasks: Clustering

We present a novel approach to perform ground-based estimation and prediction
of the surface solar irradiance with the view to predicting photovoltaic energy
production. We propose the use of mini-batch k-means clustering to extract
features, referred to as per cluster number of pixels (PCNP), from sky images
taken by a low-cost fish eye camera. These features are first used to classify
the sky as clear or cloudy using a single hidden layer neural network; the
classification accuracy achieves 99.7%. If the sky is classified as cloudy, we
propose to use a deep neural network having as input features the PCNP to
predict intra-hour variability of the solar irradiance. Toward this objective,
in this paper, we focus on estimating the deep neural network model relating
the PCNP features and the solar irradiance, which is an important step before
performing the prediction task. The proposed deep learning-based estimation
approach is shown to have an accuracy of 95%.
Exploratory Data Analysis of a Network Telescope Traffic and Prediction of Port Probing Rates
Authors: Mehdi Zakroum, Abdellah Houmz, Mounir Ghogho, Ghita Mezzour, Abdelkader Lahmadi, Jérôme François, Mohammed El Koutbi
Date: 2018-12-23 00:00:00

Understanding the properties exhibited by large scale network probing traffic
would improve cyber threat intelligence. In addition, the prediction of probing
rates is a key feature for security practitioners in their endeavors for making
better operational decisions and for enhancing their defense strategy skills.
In this work, we study different aspects of the traffic captured by a /20
network telescope. First, we perform an exploratory data analysis of the
collected probing activities. The investigation includes probing rates at the
port level, services interesting top network probers and the distribution of
probing rates by geolocation. Second, we extract the network probers
exploration patterns. We model these behaviors using transition graphs
decorated with probabilities of switching from a port to another. Finally, we
assess the capacity of Non-stationary Autoregressive and Vector Autoregressive
models in predicting port probing rates as a first step towards using more
robust models for better forecasting performance.