pwc-archive/papers-with-abstracts huggingface dataset:
Automatic segmentation of trees in dynamic outdoor environments
Authors: Amy Tabb, Henry Medeiros
Date: 2017-02-24 00:00:00
Tasks: Segmentation, Superpixels

Segmentation in dynamic outdoor environments can be difficult when the
illumination levels and other aspects of the scene cannot be controlled.
Specifically in orchard and vineyard automation contexts, a background material
is often used to shield a camera's field of view from other rows of crops. In
this paper, we describe a method that uses superpixels to determine low texture
regions of the image that correspond to the background material, and then show
how this information can be integrated with the color distribution of the image
to compute optimal segmentation parameters to segment objects of interest.
Quantitative and qualitative experiments demonstrate the suitability of this
approach for dynamic outdoor environments, specifically for tree reconstruction
and apple flower detection applications.
Privacy-Preserving Personal Model Training
Authors: Sandra Servia-Rodriguez, Liang Wang, Jianxin R. Zhao, Richard Mortier, Hamed Haddadi
Date: 2017-03-01 00:00:00
Tasks: model, Privacy Preserving

Many current Internet services rely on inferences from models trained on user
data. Commonly, both the training and inference tasks are carried out using
cloud resources fed by personal data collected at scale from users. Holding and
using such large collections of personal data in the cloud creates privacy
risks to the data subjects, but is currently required for users to benefit from
such services. We explore how to provide for model training and inference in a
system where computation is pushed to the data in preference to moving data to
the cloud, obviating many current privacy risks. Specifically, we take an
initial model learnt from a small set of users and retrain it locally using
data from a single user. We evaluate on two tasks: one supervised learning
task, using a neural network to recognise users' current activity from
accelerometer traces; and one unsupervised learning task, identifying topics in
a large set of documents. In both cases the accuracy is improved. We also
analyse the robustness of our approach against adversarial attacks, as well as
its feasibility by presenting a performance evaluation on a representative
resource-constrained device (a Raspberry Pi).
Multi-lingual neural title generation for e-Commerce browse pages
Authors: Prashant Mathur, Nicola Ueffing, Gregor Leusch
Date: 2018-04-03 00:00:00
Proceeding: NAACL 2018 6
Tasks: Transfer Learning

To provide better access of the inventory to buyers and better search engine
optimization, e-Commerce websites are automatically generating millions of
easily searchable browse pages. A browse page consists of a set of slot
name/value pairs within a given category, grouping multiple items which share
some characteristics. These browse pages require a title describing the content
of the page. Since the number of browse pages are huge, manual creation of
these titles is infeasible. Previous statistical and neural approaches depend
heavily on the availability of large amounts of data in a language. In this
research, we apply sequence-to-sequence models to generate titles for high- &
low-resourced languages by leveraging transfer learning. We train these models
on multi-lingual data, thereby creating one joint model which can generate
titles in various different languages. Performance of the title generation
system is evaluated on three different languages; English, German, and French,
with a particular focus on low-resourced French language.
MR to X-Ray Projection Image Synthesis
Authors: Bernhard Stimpel, Christopher Syben, Tobias Würfl, Katrin Mentl, Arnd Dörfler, Andreas Maier
Date: 2017-10-20 00:00:00
Tasks: Image Generation, Image-to-Image Translation, Translation

Hybrid imaging promises large potential in medical imaging applications. To
fully utilize the possibilities of corresponding information from different
modalities, the information must be transferable between the domains. In
radiation therapy planning, existing methods make use of reconstructed 3D
magnetic resonance imaging data to synthesize corresponding X-ray attenuation
maps. In contrast, for fluoroscopic procedures only line integral data, i.e.,
2D projection images, are present. The question arises which approaches could
potentially be used for this MR to X-ray projection image-to-image translation.
We examine three network architectures and two loss-functions regarding their
suitability as generator networks for this task. All generators proved to yield
suitable results for this task. A cascaded refinement network paired with a
perceptual-loss function achieved the best qualitative results in our
evaluation. The perceptual-loss showed to be able to preserve most of the
high-frequency details in the projection images and, thus, is recommended for
the underlying task and similar problems.
TVAE: Triplet-Based Variational Autoencoder using Metric Learning
Authors: Haque Ishfaq, Assaf Hoogi, Daniel Rubin
Date: 2018-02-13 00:00:00
Tasks: Metric Learning, Representation Learning, Triplet

Deep metric learning has been demonstrated to be highly effective in learning semantic representation and encoding information that can be used to measure data similarity, by relying on the embedding learned from metric learning. At the same time, variational autoencoder (VAE) has widely been used to approximate inference and proved to have a good performance for directed probabilistic models. However, for traditional VAE, the data label or feature information are intractable. Similarly, traditional representation learning approaches fail to represent many salient aspects of the data. In this project, we propose a novel integrated framework to learn latent embedding in VAE by incorporating deep metric learning. The features are learned by optimizing a triplet loss on the mean vectors of VAE in conjunction with standard evidence lower bound (ELBO) of VAE. This approach, which we call Triplet based Variational Autoencoder (TVAE), allows us to capture more fine-grained information in the latent embedding. Our model is tested on MNIST data set and achieves a high triplet accuracy of 95.60% while the traditional VAE (Kingma & Welling, 2013) achieves triplet accuracy of 75.08%.
Provably Robust Learning-Based Approach for High-Accuracy Tracking Control of Lagrangian Systems
Authors: Mohamed K. Helwa, Adam Heins, Angela P. Schoellig
Date: 2018-04-03 00:00:00
Tasks: Gaussian Processes

Lagrangian systems represent a wide range of robotic systems, including
manipulators, wheeled and legged robots, and quadrotors. Inverse dynamics
control and feedforward linearization techniques are typically used to convert
the complex nonlinear dynamics of Lagrangian systems to a set of decoupled
double integrators, and then a standard, outer-loop controller can be used to
calculate the commanded acceleration for the linearized system. However, these
methods typically depend on having a very accurate system model, which is often
not available in practice. While this challenge has been addressed in the
literature using different learning approaches, most of these approaches do not
provide safety guarantees in terms of stability of the learning-based control
system. In this paper, we provide a novel, learning-based control approach
based on Gaussian processes (GPs) that ensures both stability of the
closed-loop system and high-accuracy tracking. We use GPs to approximate the
error between the commanded acceleration and the actual acceleration of the
system, and then use the predicted mean and variance of the GP to calculate an
upper bound on the uncertainty of the linearized model. This uncertainty bound
is then used in a robust, outer-loop controller to ensure stability of the
overall system. Moreover, we show that the tracking error converges to a ball
with a radius that can be made arbitrarily small. Furthermore, we verify the
effectiveness of our approach via simulations on a 2 degree-of-freedom (DOF)
planar manipulator and experimentally on a 6 DOF industrial manipulator.
Large-Scale Cox Process Inference using Variational Fourier Features
Authors: S. T. John, James Hensman
Date: 2018-04-03 00:00:00
Proceeding: ICML 2018 7
Tasks: GPU, Small Data Image Classification

Gaussian process modulated Poisson processes provide a flexible framework for
modelling spatiotemporal point patterns. So far this had been restricted to one
dimension, binning to a pre-determined grid, or small data sets of up to a few
thousand data points. Here we introduce Cox process inference based on Fourier
features. This sparse representation induces global rather than local
constraints on the function space and is computationally efficient. This allows
us to formulate a grid-free approximation that scales well with the number of
data points and the size of the domain. We demonstrate that this allows MCMC
approximations to the non-Gaussian posterior. We also find that, in practice,
Fourier features have more consistent optimization behavior than previous
approaches. Our approximate Bayesian method can fit over 100,000 events with
complex spatiotemporal patterns in three dimensions on a single GPU.
Computer-Assisted Text Analysis for Social Science: Topic Models and Beyond
Authors: Ryan Wesslen
Date: 2018-03-29 00:00:00
Tasks: Causal Inference, Topic Models

Topic models are a family of statistical-based algorithms to summarize,
explore and index large collections of text documents. After a decade of
research led by computer scientists, topic models have spread to social science
as a new generation of data-driven social scientists have searched for tools to
explore large collections of unstructured text. Recently, social scientists
have contributed to topic model literature with developments in causal
inference and tools for handling the problem of multi-modality. In this paper,
I provide a literature review on the evolution of topic modeling including
extensions for document covariates, methods for evaluation and interpretation,
and advances in interactive visualizations along with each aspect's relevance
and application for social science research.
360° Stance Detection
Authors: Sebastian Ruder, John Glover, Afshin Mehrabani, Parsa Ghaffari
Date: 2018-04-03 00:00:00
Tasks: Stance Detection

The proliferation of fake news and filter bubbles makes it increasingly
difficult to form an unbiased, balanced opinion towards a topic. To ameliorate
this, we propose 360{\deg} Stance Detection, a tool that aggregates news with
multiple perspectives on a topic. It presents them on a spectrum ranging from
support to opposition, enabling the user to base their opinion on multiple
pieces of diverse evidence.
Simulated Autonomous Driving on Realistic Road Networks using Deep Reinforcement Learning
Authors: Patrick Klose, Rudolf Mester
Date: 2017-12-12 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Using Deep Reinforcement Learning (DRL) can be a promising approach to handle
various tasks in the field of (simulated) autonomous driving. However, recent
publications mainly consider learning in unusual driving environments. This
paper presents Driving School for Autonomous Agents (DSA^2), a software for
validating DRL algorithms in more usual driving environments based on
artificial and realistic road networks. We also present the results of applying
DSA^2 for handling the task of driving on a straight road while regulating the
velocity of one vehicle according to different speed limits.
Sparse Gaussian ICA
Authors: Nilin Abrahamsen, Philippe Rigollet
Date: 2018-04-02 00:00:00

Independent component analysis (ICA) is a cornerstone of modern data
analysis. Its goal is to recover a latent random vector S with independent
components from samples of X=AS where A is an unknown mixing matrix.
Critically, all existing methods for ICA rely on and exploit strongly the
assumption that S is not Gaussian as otherwise A becomes unidentifiable. In
this paper, we show that in fact one can handle the case of Gaussian components
by imposing structure on the matrix A. Specifically, we assume that A is sparse
and generic in the sense that it is generated from a sparse Bernoulli-Gaussian
ensemble. Under this condition, we give an efficient algorithm to recover the
columns of A given only the covariance matrix of X as input even when S has
several Gaussian components.
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
Authors: Alexander Rakhlin, Alexey Shvets, Vladimir Iglovikov, Alexandr A. Kalinin
Date: 2018-02-02 00:00:00
Tasks: Breast Cancer Detection, Breast Cancer Histology Image Classification, Classification, Diagnostic, General Classification, Histopathological Image Classification, image-classification, Image Classification, Medical Image Analysis, Sensitivity, Specificity

Breast cancer is one of the main causes of cancer death worldwide. Early
diagnostics significantly increases the chances of correct treatment and
survival, but this process is tedious and often leads to a disagreement between
pathologists. Computer-aided diagnosis systems showed potential for improving
the diagnostic accuracy. In this work, we develop the computational approach
based on deep convolution neural networks for breast cancer histology image
classification. Hematoxylin and eosin stained breast histology microscopy image
dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast
Cancer Histology Images. Our approach utilizes several deep neural network
architectures and gradient boosted trees classifier. For 4-class classification
task, we report 87.2% accuracy. For 2-class classification task to detect
carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity
96.5/88.0% at the high-sensitivity operating point. To our knowledge, this
approach outperforms other common methods in automated histopathological image
classification. The source code for our approach is made publicly available at
https://github.com/alexander-rakhlin/ICIAR2018
FlipDial: A Generative Model for Two-Way Visual Dialogue
Authors: Daniela Massiceti, N. Siddharth, Puneet K. Dokania, Philip H. S. Torr
Date: 2018-02-11 00:00:00
Proceeding: CVPR 2018 6
Tasks: Visual Dialog, Vocal Bursts Valence Prediction

We present FlipDial, a generative model for visual dialogue that
simultaneously plays the role of both participants in a visually-grounded
dialogue. Given context in the form of an image and an associated caption
summarising the contents of the image, FlipDial learns both to answer questions
and put forward questions, capable of generating entire sequences of dialogue
(question-answer pairs) which are diverse and relevant to the image. To do
this, FlipDial relies on a simple but surprisingly powerful idea: it uses
convolutional neural networks (CNNs) to encode entire dialogues directly,
implicitly capturing dialogue context, and conditional VAEs to learn the
generative model. FlipDial outperforms the state-of-the-art model in the
sequential answering task (one-way visual dialogue) on the VisDial dataset by 5
points in Mean Rank using the generated answers. We are the first to extend
this paradigm to full two-way visual dialogue, where our model is capable of
generating both questions and answers in sequence based on a visual input, for
which we propose a set of novel evaluation measures and metrics.
Exploiting temporal information for 3D pose estimation
Authors: Mir Rayat Imtiaz Hossain, James J. Little
Date: 2017-11-23 00:00:00
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Decoder, Pose Estimation

In this work, we address the problem of 3D human pose estimation from a
sequence of 2D human poses. Although the recent success of deep networks has
led many state-of-the-art methods for 3D pose estimation to train deep networks
end-to-end to predict from images directly, the top-performing approaches have
shown the effectiveness of dividing the task of 3D pose estimation into two
steps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from
images and then mapping them into 3D space. They also showed that a
low-dimensional representation like 2D locations of a set of joints can be
discriminative enough to estimate 3D pose with high accuracy. However,
estimation of 3D pose for individual frames leads to temporally incoherent
estimates due to independent error in each frame causing jitter. Therefore, in
this work we utilize the temporal information across a sequence of 2D joint
locations to estimate a sequence of 3D poses. We designed a
sequence-to-sequence network composed of layer-normalized LSTM units with
shortcut connections connecting the input to the output on the decoder side and
imposed temporal smoothness constraint during training. We found that the
knowledge of temporal consistency improves the best reported result on
Human3.6M dataset by approximately $12.2\%$ and helps our network to recover
temporally consistent 3D poses over a sequence of images even when the 2D pose
detector fails.
Statistically Motivated Second Order Pooling
Authors: Kaicheng Yu, Mathieu Salzmann
Date: 2018-01-23 00:00:00

Second-order pooling, a.k.a.~bilinear pooling, has proven effective for deep
learning based visual recognition. However, the resulting second-order networks
yield a final representation that is orders of magnitude larger than that of
standard, first-order ones, making them memory-intensive and cumbersome to
deploy. Here, we introduce a general, parametric compression strategy that can
produce more compact representations than existing compression techniques, yet
outperform both compressed and uncompressed second-order models. Our approach
is motivated by a statistical analysis of the network's activations, relying on
operations that lead to a Gaussian-distributed final representation, as
inherently used by first-order deep networks. As evidenced by our experiments,
this lets us outperform the state-of-the-art first-order and second-order
models on several benchmark recognition datasets.
CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition
Authors: Anil S. Baslamisli, Hoang-An Le, Theo Gevers
Date: 2017-12-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Deep Learning, Intrinsic Image Decomposition

Most of the traditional work on intrinsic image decomposition rely on
deriving priors about scene characteristics. On the other hand, recent research
use deep learning models as in-and-out black box and do not consider the
well-established, traditional image formation process as the basis of their
intrinsic learning process. As a consequence, although current deep learning
approaches show superior performance when considering quantitative benchmark
results, traditional approaches are still dominant in achieving high
qualitative results. In this paper, the aim is to exploit the best of the two
worlds. A method is proposed that (1) is empowered by deep learning
capabilities, (2) considers a physics-based reflection model to steer the
learning process, and (3) exploits the traditional approach to obtain intrinsic
images by exploiting reflectance and shading gradient information. The proposed
model is fast to compute and allows for the integration of all intrinsic
components. To train the new model, an object centered large-scale datasets
with intrinsic ground-truth images are created. The evaluation results
demonstrate that the new model outperforms existing methods. Visual inspection
shows that the image formation loss function augments color reproduction and
the use of gradient information produces sharper edges. Datasets, models and
higher resolution images are available at https://ivi.fnwi.uva.nl/cv/retinet.
A Constant Step Stochastic Douglas-Rachford Algorithm with Application to Non Separable Regularizations
Authors: Adil Salim, Pascal Bianchi, Walid Hachem
Date: 2018-04-03 00:00:00

The Douglas Rachford algorithm is an algorithm that converges to a minimizer
of a sum of two convex functions. The algorithm consists in fixed point
iterations involving computations of the proximity operators of the two
functions separately. The paper investigates a stochastic version of the
algorithm where both functions are random and the step size is constant. We
establish that the iterates of the algorithm stay close to the set of solution
with high probability when the step size is small enough. Application to
structured regularization is considered.
Correlated discrete data generation using adversarial training
Authors: Shreyas Patel, Ashutosh Kakadiya, Maitrey Mehta, Raj Derasari, Rahul Patel, Ratnik Gandhi
Date: 2018-04-03 00:00:00
Tasks: Anomaly Detection, Image Generation, Image Inpainting, Style Transfer

Generative Adversarial Networks (GAN) have shown great promise in tasks like
synthetic image generation, image inpainting, style transfer, and anomaly
detection. However, generating discrete data is a challenge. This work presents
an adversarial training based correlated discrete data (CDD) generation model.
It also details an approach for conditional CDD generation. The results of our
approach are presented over two datasets; job-seeking candidates skill set
(private dataset) and MNIST (public dataset). From quantitative and qualitative
analysis of these results, we show that our model performs better as it
leverages inherent correlation in the data, than an existing model that
overlooks correlation.
DeSIGN: Design Inspiration from Generative Networks
Authors: Othman Sbai, Mohamed Elhoseiny, Antoine Bordes, Yann Lecun, Camille Couprie
Date: 2018-04-03 00:00:00
Tasks: Image Generation, Retrieval

Can an algorithm create original and compelling fashion designs to serve as
an inspirational assistant? To help answer this question, we design and
investigate different image generation models associated with different loss
functions to boost creativity in fashion generation. The dimensions of our
explorations include: (i) different Generative Adversarial Networks
architectures that start from noise vectors to generate fashion items, (ii)
novel loss functions that encourage novelty, inspired from Sharma-Mittal
divergence, a generalized mutual information measure for the widely used
relative entropies such as Kullback-Leibler, and (iii) a generation process
following the key elements of fashion design (disentangling shape and texture
components). A key challenge of this study is the evaluation of generated
designs and the retrieval of best ones, hence we put together an evaluation
protocol associating automatic metrics and human experimental studies that we
hope will help ease future research. We show that our proposed creativity
criterion yield better overall appreciation than the one employed in Creative
Adversarial Networks. In the end, about 61% of our images are thought to be
created by human designers rather than by a computer while also being
considered original per our human subject experiments, and our proposed loss
scores the highest compared to existing losses in both novelty and likability.
Speech waveform synthesis from MFCC sequences with generative adversarial networks
Authors: Lauri Juvela, Bajibabu Bollepalli, Xin Wang, Hirokazu Kameoka, Manu Airaksinen, Junichi Yamagishi, Paavo Alku
Date: 2018-04-03 00:00:00
Tasks: Generative Adversarial Network, Speech Synthesis

This paper proposes a method for generating speech from filterbank mel
frequency cepstral coefficients (MFCC), which are widely used in speech
applications, such as ASR, but are generally considered unusable for speech
synthesis. First, we predict fundamental frequency and voicing information from
MFCCs with an autoregressive recurrent neural net. Second, the spectral
envelope information contained in MFCCs is converted to all-pole filters, and a
pitch-synchronous excitation model matched to these filters is trained.
Finally, we introduce a generative adversarial network -based noise model to
add a realistic high-frequency stochastic component to the modeled excitation
signal. The results show that high quality speech reconstruction can be
obtained, given only MFCC information at test time.