pwc-archive/papers-with-abstracts huggingface dataset:
Automated Phenotyping of Epicuticular Waxes of Grapevine Berries Using Light Separation and Convolutional Neural Networks
Authors: Pierre Barré, Katja Herzog, Rebecca Höfle, Matthias B. Hullin, Reinhard Töpfer, Volker Steinhage
Date: 2018-07-19 00:00:00

In viticulture the epicuticular wax as the outer layer of the berry skin is
known as trait which is correlated to resilience towards Botrytis bunch rot.
Traditionally this trait is classified using the OIV descriptor 227 (berry
bloom) in a time consuming way resulting in subjective and error-prone
phenotypic data. In the present study an objective, fast and sensor-based
approach was developed to monitor berry bloom. From the technical
point-of-view, it is known that the measurement of different illumination
components conveys important information about observed object surfaces. A
Mobile Light-Separation-Lab is proposed in order to capture
illumination-separated images of grapevine berries for phenotyping the
distribution of epicuticular waxes (berry bloom). For image analysis, an
efficient convolutional neural network approach is used to derive the
uniformity and intactness of waxes on berries. Method validation over six
grapevine cultivars shows accuracies up to $97.3$%. In addition, electrical
impedance of the cuticle and its epicuticular waxes (described as an indicator
for the thickness of berry skin and its permeability) was correlated to the
detected proportion of waxes with $r=0.76$. This novel, fast and non-invasive
phenotyping approach facilitates enlarged screenings within grapevine breeding
material and genetic repositories regarding berry bloom characteristics and its
impact on resilience towards Botrytis bunch rot.
Sequence to Logic with Copy and Cache
Authors: Javid Dadashkarimi, Sekhar Tatikonda
Date: 2018-07-19 00:00:00
Tasks: Decoder, Form

Generating logical form equivalents of human language is a fresh way to
employ neural architectures where long short-term memory effectively captures
dependencies in both encoder and decoder units.
  The logical form of the sequence usually preserves information from the
natural language side in the form of similar tokens, and recently a copying
mechanism has been proposed which increases the probability of outputting
tokens from the source input through decoding.
  In this paper we propose a caching mechanism as a more general form of the
copying mechanism which also weighs all the words from the source vocabulary
according to their relation to the current decoding context.
  Our results confirm that the proposed method achieves improvements in
sequence/token-level accuracy on sequence to logical form tasks. Further
experiments on cross-domain adversarial attacks show substantial improvements
when using the most influential examples of other domains for training.
Deep Adaptive Proposal Network for Object Detection in Optical Remote Sensing Images
Authors: Lin Cheng, Xu Liu, Lingling Li, Licheng Jiao, Xu Tang
Date: 2018-07-19 00:00:00
Tasks: Object, object-detection, Object Detection, Region Proposal

Object detection is a fundamental and challenging problem in aerial and
satellite image analysis. More recently, a two-stage detector Faster R-CNN is
proposed and demonstrated to be a promising tool for object detection in
optical remote sensing images, while the sparse and dense characteristic of
objects in remote sensing images is complexity. It is unreasonable to treat all
images with the same region proposal strategy, and this treatment limits the
performance of two-stage detectors. In this paper, we propose a novel and
effective approach, named deep adaptive proposal network (DAPNet), address this
complexity characteristic of object by learning a new category prior network
(CPN) on the basis of the existing Faster R-CNN architecture. Moreover, the
candidate regions produced by DAPNet model are different from the traditional
region proposal network (RPN), DAPNet predicts the detail category of each
candidate region. And these candidate regions combine the object number, which
generated by the category prior network to achieve a suitable number of
candidate boxes for each image. These candidate boxes can satisfy detection
tasks in sparse and dense scenes. The performance of the proposed framework has
been evaluated on the challenging NWPU VHR-10 data set. Experimental results
demonstrate the superiority of the proposed framework to the state-of-the-art.
Attend and Rectify: a Gated Attention Mechanism for Fine-Grained Recovery
Authors: Pau Rodríguez, Josep M. Gonfaus, Guillem Cucurull, F. Xavier Roca, Jordi Gonzàlez
Date: 2018-07-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Classification, General Classification, Image Classification

We propose a novel attention mechanism to enhance Convolutional Neural
Networks for fine-grained recognition. It learns to attend to lower-level
feature activations without requiring part annotations and uses these
activations to update and rectify the output likelihood distribution. In
contrast to other approaches, the proposed mechanism is modular,
architecture-independent and efficient both in terms of parameters and
computation required. Experiments show that networks augmented with our
approach systematically improve their classification accuracy and become more
robust to clutter. As a result, Wide Residual Networks augmented with our
proposal surpasses the state of the art classification accuracies in CIFAR-10,
the Adience gender recognition task, Stanford dogs, and UEC Food-100.
Bounded Information Rate Variational Autoencoders
Authors: D. T. Braithwaite, W. B. Kleijn
Date: 2018-07-19 00:00:00

This paper introduces a new member of the family of Variational Autoencoders
(VAE) that constrains the rate of information transferred by the latent layer.
The latent layer is interpreted as a communication channel, the information
rate of which is bound by imposing a pre-set signal-to-noise ratio. The new
constraint subsumes the mutual information between the input and latent
variables, combining naturally with the likelihood objective of the observed
data as used in a conventional VAE. The resulting Bounded-Information-Rate
Variational Autoencoder (BIR-VAE) provides a meaningful latent representation
with an information resolution that can be specified directly in bits by the
system designer. The rate constraint can be used to prevent overtraining, and
the method naturally facilitates quantisation of the latent variables at the
set rate. Our experiments confirm that the BIR-VAE has a meaningful latent
representation and that its performance is at least as good as state-of-the-art
competing algorithms, but with lower computational complexity.
Online Evaluations for Everyone: Mr. DLib's Living Lab for Scholarly Recommendations
Authors: Joeran Beel, Andrew Collins, Oliver Kopp, Linus W. Dietz, Petr Knoth
Date: 2018-07-19 00:00:00
Tasks: Management, Recommendation Systems

We introduce the first 'living lab' for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., recommendations for research papers, citations, conferences, research grants, etc. Recommendations are delivered through the living lab's API to platforms such as reference management software and digital libraries. The living lab is built on top of the recommender-system as-a-service Mr. DLib. Current partners are the reference management software JabRef and the CORE research team. We present the architecture of Mr. DLib's living lab as well as usage statistics on the first sixteen months of operating it. During this time, 1,826,643 recommendations were delivered with an average click-through rate of 0.21%.
Operator-in-the-Loop Deep Sequential Multi-camera Feature Fusion for Person Re-identification
Authors: K L Navaneet, Ravi Kiran Sarvadevabhatla, Shashank Shekhar, R. Venkatesh Babu, Anirban Chakraborty
Date: 2018-07-19 00:00:00
Tasks: Person Re-Identification

Given a target image as query, person re-identification systems retrieve a ranked list of candidate matches on a per-camera basis. In deployed systems, a human operator scans these lists and labels sighted targets by touch or mouse-based selection. However, classical re-id approaches generate per-camera lists independently. Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras. To address this shortcoming, we propose a novel sequential multi-camera re-id approach. The proposed approach can accommodate human operator inputs and provides early gains via a monotonic improvement in target ranking. At the heart of our approach is a fusion function which operates on deep feature representations of query and candidate matches. We formulate an optimization procedure custom-designed to incrementally improve query representation. Since existing evaluation methods cannot be directly adopted to our setting, we also propose two novel evaluation protocols. The results on two large-scale re-id datasets (Market-1501, DukeMTMC-reID) demonstrate that our multi-camera method significantly outperforms baselines and other popular feature fusion schemes. Additionally, we conduct a comparative subject-based study of human operator performance. The superior operator performance enabled by our approach makes a compelling case for its integration into deployable video-surveillance systems.
Online Label Aggregation: A Variational Bayesian Approach
Authors: Chi Hong, Amirmasoud Ghiassi, Yichi Zhou, Robert Birke, Lydia Y. Chen
Date: 2018-07-19 00:00:00
Tasks: Bayesian Inference, Stochastic Optimization

Noisy labeled data is more a norm than a rarity for crowd sourced contents. It is effective to distill noise and infer correct labels through aggregation results from crowd workers. To ensure the time relevance and overcome slow responses of workers, online label aggregation is increasingly requested, calling for solutions that can incrementally infer true label distribution via subsets of data items. In this paper, we propose a novel online label aggregation framework, BiLA, which employs variational Bayesian inference method and designs a novel stochastic optimization scheme for incremental training. BiLA is flexible to accommodate any generating distribution of labels by the exact computation of its posterior distribution. We also derive the convergence bound of the proposed optimizer. We compare BiLA with the state of the art based on minimax entropy, neural networks and expectation maximization algorithms, on synthetic and real-world data sets. Our evaluation results on various online scenarios show that BiLA can effectively infer the true labels, with an error rate reduction of at least 10 to 1.5 percent points for synthetic and real-world datasets, respectively.
In pixels we trust: From Pixel Labeling to Object Localization and Scene Categorization
Authors: Carlos Herranz-Perdiguero, Carolina Redondo-Cabrera, Roberto J. López-Sastre
Date: 2018-07-19 00:00:00
Tasks: object-detection, Object Detection, Object Localization, Scene Classification, Scene Understanding, Segmentation, Semantic Segmentation

While there has been significant progress in solving the problems of image
pixel labeling, object detection and scene classification, existing approaches
normally address them separately. In this paper, we propose to tackle these
problems from a bottom-up perspective, where we simply need a semantic
segmentation of the scene as input. We employ the DeepLab architecture, based
on the ResNet deep network, which leverages multi-scale inputs to later fuse
their responses to perform a precise pixel labeling of the scene. This semantic
segmentation mask is used to localize the objects and to recognize the scene,
following two simple yet effective strategies. We evaluate the benefits of our
solutions, performing a thorough experimental evaluation on the NYU Depth V2
dataset. Our approach achieves a performance that beats the leading results by
a significant margin, defining the new state of the art in this benchmark for
the three tasks comprising the scene understanding: semantic segmentation,
object detection and scene categorization.
Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization
Authors: Dmitry Shalyga, Pavel Filonov, Andrey Lavrentyev
Date: 2018-07-19 00:00:00
Tasks: Anomaly Detection

We continue to develop our neural network (NN) based forecasting approach to
anomaly detection (AD) using the Secure Water Treatment (SWaT) industrial
control system (ICS) testbed dataset. We propose genetic algorithms (GA) to
find the best NN architecture for a given dataset, using the NAB metric to
assess the quality of different architectures. The drawbacks of the F1-metric
are analyzed. Several techniques are proposed to improve the quality of AD:
exponentially weighted smoothing, mean p-powered error measure, individual
error weight for each variable, disjoint prediction windows. Based on the
techniques used, an approach to anomaly interpretation is introduced.
ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
Authors: Wei Ping, Kainan Peng, Jitong Chen
Date: 2018-07-19 00:00:00
Proceeding: ICLR 2019 5
Tasks: Speech Synthesis, text-to-speech, Text to Speech

In this work, we propose a new solution for parallel wave generation by
WaveNet. In contrast to parallel WaveNet (van den Oord et al., 2018), we
distill a Gaussian inverse autoregressive flow from the autoregressive WaveNet
by minimizing a regularized KL divergence between their highly-peaked output
distributions. Our method computes the KL divergence in closed-form, which
simplifies the training algorithm and provides very efficient distillation. In
addition, we introduce the first text-to-wave neural architecture for speech
synthesis, which is fully convolutional and enables fast end-to-end training
from scratch. It significantly outperforms the previous pipeline that connects
a text-to-spectrogram model to a separately trained WaveNet (Ping et al.,
2018). We also successfully distill a parallel waveform synthesizer conditioned
on the hidden representation in this end-to-end model.
Imparting Interpretability to Word Embeddings while Preserving Semantic Structure
Authors: Lutfi Kerem Senel, Ihsan Utlu, Furkan Şahinuç, Haldun M. Ozaktas, Aykut Koç
Date: 2018-07-19 00:00:00
Tasks: Word Embeddings, Word Similarity

As an ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representation. They capture semantic and syntactic relations among words but the vectors corresponding to the words are only meaningful relative to each other. Neither the vector nor its dimensions have any absolute, interpretable meaning. We introduce an additive modification to the objective function of the embedding learning algorithm that encourages the embedding vectors of words that are semantically related to a predefined concept to take larger values along a specified dimension, while leaving the original semantic learning mechanism mostly unaffected. In other words, we align words that are already determined to be related, along predefined concepts. Therefore, we impart interpretability to the word embedding by assigning meaning to its vector dimensions. The predefined concepts are derived from an external lexical resource, which in this paper is chosen as Roget's Thesaurus. We observe that alignment along the chosen concepts is not limited to words in the Thesaurus and extends to other related words as well. We quantify the extent of interpretability and assignment of meaning from our experimental results. Manual human evaluation results have also been presented to further verify that the proposed method increases interpretability. We also demonstrate the preservation of semantic coherence of the resulting vector space by using word-analogy and word-similarity tests. These tests show that the interpretability-imparted word embeddings that are obtained by the proposed framework do not sacrifice performances in common benchmark tests.
Visual Domain Adaptation with Manifold Embedded Distribution Alignment
Authors: Jindong Wang, Wenjie Feng, Yiqiang Chen, Han Yu, Meiyu Huang, Philip S. Yu
Date: 2018-07-19 00:00:00
Tasks: Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

Visual domain adaptation aims to learn robust classifiers for the target
domain by leveraging knowledge from a source domain. Existing methods either
attempt to align the cross-domain distributions, or perform manifold subspace
learning. However, there are two significant challenges: (1) degenerated
feature transformation, which means that distribution alignment is often
performed in the original feature space, where feature distortions are hard to
overcome. On the other hand, subspace learning is not sufficient to reduce the
distribution divergence. (2) unevaluated distribution alignment, which means
that existing distribution alignment methods only align the marginal and
conditional distributions with equal importance, while they fail to evaluate
the different importance of these two distributions in real applications. In
this paper, we propose a Manifold Embedded Distribution Alignment (MEDA)
approach to address these challenges. MEDA learns a domain-invariant classifier
in Grassmann manifold with structural risk minimization, while performing
dynamic distribution alignment to quantitatively account for the relative
importance of marginal and conditional distributions. To the best of our
knowledge, MEDA is the first attempt to perform dynamic distribution alignment
for manifold domain adaptation. Extensive experiments demonstrate that MEDA
shows significant improvements in classification accuracy compared to
state-of-the-art traditional and deep methods.
Towards Explainable and Controllable Open Domain Dialogue Generation with Dialogue Acts
Authors: Can Xu, Wei Wu, Yu Wu
Date: 2018-07-19 00:00:00
Tasks: Dialogue Generation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Response Generation

We study open domain dialogue generation with dialogue acts designed to
explain how people engage in social chat. To imitate human behavior, we propose
managing the flow of human-machine interactions with the dialogue acts as
policies. The policies and response generation are jointly learned from
human-human conversations, and the former is further optimized with a
reinforcement learning approach. With the dialogue acts, we achieve significant
improvement over state-of-the-art methods on response quality for given
contexts and dialogue length in both machine-machine simulation and
human-machine conversation.
Chest X-rays Classification: A Multi-Label and Fine-Grained Problem
Authors: Zongyuan Ge, Dwarikanath Mahapatra, Suman Sedai, Rahil Garnavi, Rajib Chakravorty
Date: 2018-07-19 00:00:00
Tasks: General Classification, image-classification, Image Classification, Medical Image Classification

The widely used ChestX-ray14 dataset addresses an important medical image
classification problem and has the following caveats: 1) many lung pathologies
are visually similar, 2) a variant of diseases including lung cancer,
tuberculosis, and pneumonia are present in a single scan, i.e. multiple labels
and 3) The incidence of healthy images is much larger than diseased samples,
creating imbalanced data. These properties are common in medical domain.
Existing literature uses stateof- the-art DensetNet/Resnet models being
transfer learned where output neurons of the networks are trained for
individual diseases to cater for multiple diseases labels in each image.
However, most of them don't consider relationship between multiple classes. In
this work we have proposed a novel error function, Multi-label Softmax Loss
(MSML), to specifically address the properties of multiple labels and
imbalanced data. Moreover, we have designed deep network architecture based on
fine-grained classification concept that incorporates MSML. We have evaluated
our proposed method on various network backbones and showed consistent
performance improvements of AUC-ROC scores on the ChestX-ray14 dataset. The
proposed error function provides a new method to gain improved performance
across wider medical datasets.
ArticulatedFusion: Real-time Reconstruction of Motion, Geometry and Segmentation Using a Single Depth Camera
Authors: Chao Li, Zheheng Zhao, Xiaohu Guo
Date: 2018-07-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Segmentation

This paper proposes a real-time dynamic scene reconstruction method capable
of reproducing the motion, geometry, and segmentation simultaneously given live
depth stream from a single RGB-D camera. Our approach fuses geometry frame by
frame and uses a segmentation-enhanced node graph structure to drive the
deformation of geometry in registration step. A two-level node motion
optimization is proposed. The optimization space of node motions and the range
of physically-plausible deformations are largely reduced by taking advantage of
the articulated motion prior, which is solved by an efficient node graph
segmentation method. Compared to previous fusion-based dynamic scene
reconstruction methods, our experiments show robust and improved reconstruction
results for tangential and occluded motions.
Monocular Object Orientation Estimation using Riemannian Regression and Classification Networks
Authors: Siddharth Mahendran, Ming Yang Lu, Haider Ali, René Vidal
Date: 2018-07-19 00:00:00
Tasks: Classification, Data Augmentation, General Classification, regression

We consider the task of estimating the 3D orientation of an object of known
category given an image of the object and a bounding box around it. Recently,
CNN-based regression and classification methods have shown significant
performance improvements for this task. This paper proposes a new CNN-based
approach to monocular orientation estimation that advances the state of the art
in four different directions. First, we take into account the Riemannian
structure of the orientation space when designing regression losses and
nonlinear activation functions. Second, we propose a mixed Riemannian
regression and classification framework that better handles the challenging
case of nearly symmetric objects. Third, we propose a data augmentation
strategy that is specifically designed to capture changes in 3D orientation.
Fourth, our approach leads to state-of-the-art results on the PASCAL3D+
dataset.
Deconfounding age effects with fair representation learning when assessing dementia
Authors: Zining Zhu, Jekaterina Novikova, Frank Rudzicz
Date: 2018-07-19 00:00:00
Tasks: Representation Learning

One of the most prevalent symptoms among the elderly population, dementia, can be detected by classifiers trained on linguistic features extracted from narrative transcripts. However, these linguistic features are impacted in a similar but different fashion by the normal aging process. Aging is therefore a confounding factor, whose effects have been hard for machine learning classifiers (especially deep neural network based models) to ignore. We show DNN models are capable of estimating ages based on linguistic features. Predicting dementia based on this aging bias could lead to potentially non-generalizable accuracies on clinical datasets, if not properly deconfounded. In this paper, we propose to address this deconfounding problem with fair representation learning. We build neural network classifiers that learn low-dimensional representations reflecting the impacts of dementia yet discarding the effects of age. To evaluate these classifiers, we specify a model-agnostic score $\Delta_{eo}^{(N)}$ measuring how classifier results are deconfounded from age. Our best models compromise accuracy by only 2.56\% and 1.54\% on two clinical datasets compared to DNNs, and their $\Delta_{eo}^{(2)}$ scores are better than statistical (residulization and inverse probability weight) adjustments.