pwc-archive/papers-with-abstracts huggingface dataset:
Online Adaptation through Meta-Learning for Stereo Depth Estimation
Authors: Zhen-Yu Zhang, Stéphane Lathuilière, Andrea Pilzer, Nicu Sebe, Elisa Ricci, Jian Yang
Date: 2019-04-17 00:00:00
Tasks: Depth Estimation, Meta-Learning, Stereo Depth Estimation

In this work, we tackle the problem of online adaptation for stereo depth
estimation, that consists in continuously adapting a deep network to a target
video recordedin an environment different from that of the source training set.
To address this problem, we propose a novel Online Meta-Learning model with
Adaption (OMLA). Our proposal is based on two main contributions. First, to
reducethe domain-shift between source and target feature distributions we
introduce an online feature alignment procedurederived from Batch
Normalization. Second, we devise a meta-learning approach that exploits feature
alignment forfaster convergence in an online learning setting. Additionally, we
propose a meta-pre-training algorithm in order toobtain initial network weights
on the source dataset whichfacilitate adaptation on future data streams.
Experimentally, we show that both OMLA and meta-pre-training helpthe model to
adapt faster to a new environment. Our proposal is evaluated on the
wellestablished KITTI dataset,where we show that our online method is
competitive withstate of the art algorithms trained in a batch setting.
Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs Models
Authors: Hieu Quang Nguyen, Abdul Hasib Rahimyar, Xiaodi Wang
Date: 2019-04-17 00:00:00

The task of predicting future stock values has always been one that is
heavily desired albeit very difficult. This difficulty arises from stocks with
non-stationary behavior, and without any explicit form. Hence, predictions are
best made through analysis of financial stock data. To handle big data sets,
current convention involves the use of the Moving Average. However, by
utilizing the Wavelet Transform in place of the Moving Average to denoise stock
signals, financial data can be smoothened and more accurately broken down. This
newly transformed, denoised, and more stable stock data can be followed up by
non-parametric statistical methods, such as Support Vector Regression (SVR) and
Recurrent Neural Network (RNN) based Long Short-Term Memory (LSTM) networks to
predict future stock prices. Through the implementation of these methods, one
is left with a more accurate stock forecast, and in turn, increased profits.
Headline Generation: Learning from Decomposable Document Titles
Authors: Oleg Vasilyev, Tom Grek, John Bohannon
Date: 2019-04-17 00:00:00
Tasks: Articles, Headline Generation, Question Answering

We propose a novel method for generating titles for unstructured text documents. We reframe the problem as a sequential question-answering task. A deep neural network is trained on document-title pairs with decomposable titles, meaning that the vocabulary of the title is a subset of the vocabulary of the document. To train the model we use a corpus of millions of publicly available document-title pairs: news articles and headlines. We present the results of a randomized double-blind trial in which subjects were unaware of which titles were human or machine-generated. When trained on approximately 1.5 million news articles, the model generates headlines that humans judge to be as good or better than the original human-written headlines in the majority of cases.
Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks
Authors: Zhenyu Tang, John D. Kanu, Kevin Hogan, Dinesh Manocha
Date: 2019-04-17 00:00:00
Tasks: Direction of Arrival Estimation, General Classification, regression

We present a novel learning-based approach to estimate the direction-of-arrival (DOA) of a sound source using a convolutional recurrent neural network (CRNN) trained via regression on synthetic data and Cartesian labels. We also describe an improved method to generate synthetic data to train the neural network using state-of-the-art sound propagation algorithms that model specular as well as diffuse reflections of sound. We compare our model against three other CRNNs trained using different formulations of the same problem: classification on categorical labels, and regression on spherical coordinate labels. In practice, our model achieves up to 43% decrease in angular error over prior methods. The use of diffuse reflection results in 34% and 41% reduction in angular prediction errors on LOCATA and SOFA datasets, respectively, over prior methods based on image-source methods. Our method results in an additional 3% error reduction over prior schemes that use classification based networks, and we use 36% fewer network parameters.
Defensive Quantization: When Efficiency Meets Robustness
Authors: Ji Lin, Chuang Gan, Song Han
Date: 2019-04-17 00:00:00
Proceeding: ICLR 2019 5
Tasks: Adversarial Attack, CPU, GPU, Quantization

Neural network quantization is becoming an industry standard to efficiently
deploy deep learning models on hardware platforms, such as CPU, GPU, TPU, and
FPGAs. However, we observe that the conventional quantization approaches are
vulnerable to adversarial attacks. This paper aims to raise people's awareness
about the security of the quantized models, and we designed a novel
quantization methodology to jointly optimize the efficiency and robustness of
deep learning models. We first conduct an empirical study to show that vanilla
quantization suffers more from adversarial attacks. We observe that the
inferior robustness comes from the error amplification effect, where the
quantization operation further enlarges the distance caused by amplified noise.
Then we propose a novel Defensive Quantization (DQ) method by controlling the
Lipschitz constant of the network during quantization, such that the magnitude
of the adversarial noise remains non-expansive during inference. Extensive
experiments on CIFAR-10 and SVHN datasets demonstrate that our new quantization
method can defend neural networks against adversarial examples, and even
achieves superior robustness than their full-precision counterparts while
maintaining the same hardware efficiency as vanilla quantization approaches. As
a by-product, DQ can also improve the accuracy of quantized models without
adversarial attack.
A large-scale field test on word-image classification in large historical document collections using a traditional and two deep-learning methods
Authors: Lambert Schomaker
Date: 2019-04-17 00:00:00
Tasks: General Classification, image-classification, Image Classification

This technical report describes a practical field test on word-image
classification in a very large collection of more than 300 diverse handwritten
historical manuscripts, with 1.6 million unique labeled images and more than 11
million images used in testing. Results indicate that several deep-learning
tests completely failed (mean accuracy 83%). In the tests with more than 1000
output units (lexical words) in one-hot encoding for classification,
performance steeply drops to almost zero percent accuracy, even with a modest
size of the pre-final (i.e., penultimate) layer (150 units). A traditional
feature method (BOVW) displays a consistent performance over numbers of classes
and numbers of training examples (mean accuracy 87%). Additional tests using
nearest mean on the output of the pre-final layer of an Inception V3 network,
for each book, only yielded mediocre results (mean accuracy 49\%), but was not
sensitive to high numbers of classes. Notably, this experiment was only
possible on the basis of labels that were harvested on the basis of a
traditional method which already works starting from a single labeled image per
class. It is expected that the performance of the failed deep learning tests
can be repaired, but only on the basis of human handcrafting (sic) of network
architecture and hyperparameters. When the failed problematic books are not
considered, end-to-end CNN training yields about 95% accuracy. This average is
dominated by a large subset of Chinese characters, performances for other
script styles being lower.
True Parallel Graph Transformations: an Algebraic Approach Based on Weak Spans
Authors: Thierry Boy de la Tour, Rachid Echahed
Date: 2019-04-17 00:00:00

We address the problem of defining graph transformations by the simultaneous
application of direct transformations even when these cannot be applied
independently of each other. An algebraic approach is adopted, with production
rules of the form $L\xleftarrow{l}K \xleftarrow{i} I \xrightarrow{r} R$, called
weak spans. A parallel coherent transformation is introduced and shown to be a
conservative extension of the interleaving semantics of parallel independent
direct transformations. A categorical construction of finitely attributed
structures is proposed, in which parallel coherent transformations can be built
in a natural way. These notions are introduced and illustrated on detailed
examples.
An efficient multi-language Video Search Engine to facilitate the HADJ and the UMRA
Authors: Mohamed Hamroun, Sonia Lajmi
Date: 2019-04-17 00:00:00

Videos clips became the most important and prominent multimedia document to
illustrate the rituals process of Hajj and Umrah. Therefore, it is necessary to
develop a system to facilitate access to information related to the duties, the
pillars, the stages and the prayers. In this paper present a new project
accomplishing a search engine in a large video database enabling any pilgrims
to get the information that he care about as fast, accurate. This project is
based on two techniques: (a) the weighting method to determine the degree of
affiliation of a video clip to a particular topic (b) organizing data using
several layers.
An Exponential Lower Bound for the Runtime of the cGA on Jump Functions
Authors: Benjamin Doerr
Date: 2019-04-17 00:00:00

In the first runtime analysis of an estimation-of-distribution algorithm (EDA) on the multi-modal jump function class, Hasen\"ohrl and Sutton (GECCO 2018) proved that the runtime of the compact genetic algorithm with suitable parameter choice on jump functions with high probability is at most polynomial (in the dimension) if the jump size is at most logarithmic (in the dimension), and is at most exponential in the jump size if the jump size is super-logarithmic. The exponential runtime guarantee was achieved with a hypothetical population size that is also exponential in the jump size. Consequently, this setting cannot lead to a better runtime. In this work, we show that any choice of the hypothetical population size leads to a runtime that, with high probability, is at least exponential in the jump size. This result might be the first non-trivial exponential lower bound for EDAs that holds for arbitrary parameter settings.
2D Car Detection in Radar Data with PointNets
Authors: Andreas Danzer, Thomas Griebel, Martin Bach, Klaus Dietmayer
Date: 2019-04-17 00:00:00
Tasks: 2D Object Detection, Classification, General Classification, Object, object-detection, Object Detection, RGB-D Salient Object Detection

For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to detect 2D objects solely depending on sparse radar data using PointNets. In literature, only methods are presented so far which perform either object classification or bounding box estimation for objects. In contrast, this method facilitates a classification together with a bounding box estimation of objects using a single radar sensor. To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal 2D bounding box. The algorithm is evaluated using an automatically created dataset which consist of various realistic driving maneuvers. The results show the great potential of object detection in high-resolution radar data using PointNets.
Few Shot Speaker Recognition using Deep Neural Networks
Authors: Prashant Anand, Ajeet kumar Singh, Siddharth Srivastava, Brejesh lall
Date: 2019-04-17 00:00:00
Tasks: Few-Shot Learning, Speaker Recognition

The recent advances in deep learning are mostly driven by availability of
large amount of training data. However, availability of such data is not always
possible for specific tasks such as speaker recognition where collection of
large amount of data is not possible in practical scenarios. Therefore, in this
paper, we propose to identify speakers by learning from only a few training
examples. To achieve this, we use a deep neural network with prototypical loss
where the input to the network is a spectrogram. For output, we project the
class feature vectors into a common embedding space, followed by
classification. Further, we show the effectiveness of capsule net in a few shot
learning setting. To this end, we utilize an auto-encoder to learn generalized
feature embeddings from class-specific embeddings obtained from capsule
network. We provide exhaustive experiments on publicly available datasets and
competitive baselines, demonstrating the superiority and generalization ability
of the proposed few shot learning pipelines.
SCE: A manifold regularized set-covering method for data partitioning
Authors: Xuelong. Li, Quanmao Lu, Yongsheng Dong, DaCheng Tao
Date: 2019-04-17 00:00:00
Tasks: Clustering

Cluster analysis plays a very important role in data analysis. In these
years, cluster ensemble, as a cluster analysis tool, has drawn much attention
for its robustness, stability, and accuracy. Many efforts have been done to
combine different initial clustering results into a single clustering solution
with better performance. However, they neglect the structure information of the
raw data in performing the cluster ensemble. In this paper, we propose a
Structural Cluster Ensemble (SCE) algorithm for data partitioning formulated as
a set-covering problem. In particular, we construct a Laplacian regularized
objective function to capture the structure information among clusters.
Moreover, considering the importance of the discriminative information
underlying in the initial clustering results, we add a discriminative
constraint into our proposed objective function. Finally, we verify the
performance of the SCE algorithm on both synthetic and real data sets. The
experimental results show the effectiveness of our proposed method SCE
algorithm.
Neural Painters: A learned differentiable constraint for generating brushstroke paintings
Authors: Reiichiro Nakano
Date: 2019-04-17 00:00:00
Tasks: Style Transfer

We explore neural painters, a generative model for brushstrokes learned from
a real non-differentiable and non-deterministic painting program. We show that
when training an agent to "paint" images using brushstrokes, using a
differentiable neural painter leads to much faster convergence. We propose a
method for encouraging this agent to follow human-like strokes when
reconstructing digits. We also explore the use of a neural painter as a
differentiable image parameterization. By directly optimizing brushstrokes to
activate neurons in a pre-trained convolutional network, we can directly
visualize ImageNet categories and generate "ideal" paintings of each class.
Finally, we present a new concept called intrinsic style transfer. By
minimizing only the content loss from neural style transfer, we allow the
artistic medium, in this case, brushstrokes, to naturally dictate the resulting
style.
Mining Closed Episodes with Simultaneous Events
Authors: Nikolaj Tatti, Boris Cule
Date: 2019-04-16 00:00:00

Sequential pattern discovery is a well-studied field in data mining. Episodes
are sequential patterns describing events that often occur in the vicinity of
each other. Episodes can impose restrictions to the order of the events, which
makes them a versatile technique for describing complex patterns in the
sequence. Most of the research on episodes deals with special cases such as
serial, parallel, and injective episodes, while discovering general episodes is
understudied.
  In this paper we extend the definition of an episode in order to be able to
represent cases where events often occur simultaneously. We present an
efficient and novel miner for discovering frequent and closed general episodes.
Such a task presents unique challenges. Firstly, we cannot define closure based
on frequency. We solve this by computing a more conservative closure that we
use to reduce the search space and discover the closed episodes as a
postprocessing step. Secondly, episodes are traditionally presented as directed
acyclic graphs. We argue that this representation has drawbacks leading to
redundancy in the output. We solve these drawbacks by defining a subset
relationship in such a way that allows us to remove the redundant episodes. We
demonstrate the efficiency of our algorithm and the need for using closed
episodes empirically on synthetic and real-world datasets.
Fast object detection in compressed JPEG Images
Authors: Benjamin Deguerre, Clément Chatelain, Gilles Gasso
Date: 2019-04-16 00:00:00
Tasks: Object, object-detection, Object Detection, Real-Time Object Detection

Object detection in still images has drawn a lot of attention over past few
years, and with the advent of Deep Learning impressive performances have been
achieved with numerous industrial applications. Most of these deep learning
models rely on RGB images to localize and identify objects in the image.
However in some application scenarii, images are compressed either for storage
savings or fast transmission. Therefore a time consuming image decompression
step is compulsory in order to apply the aforementioned deep models. To
alleviate this drawback, we propose a fast deep architecture for object
detection in JPEG images, one of the most widespread compression format. We
train a neural network to detect objects based on the blockwise DCT (discrete
cosine transform) coefficients {issued from} the JPEG compression algorithm. We
modify the well-known Single Shot multibox Detector (SSD) by replacing its
first layers with one convolutional layer dedicated to process the DCT inputs.
Experimental evaluations on PASCAL VOC and industrial dataset comprising images
of road traffic surveillance show that the model is about $2\times$ faster than
regular SSD with promising detection performances. To the best of our
knowledge, this paper is the first to address detection in compressed JPEG
images.
An Empirical Evaluation of Text Representation Schemes on Multilingual Social Web to Filter the Textual Aggression
Authors: Sandip Modha, Prasenjit Majumder
Date: 2019-04-16 00:00:00
Tasks: General Classification, Sentence, Transfer Learning

This paper attempt to study the effectiveness of text representation schemes
on two tasks namely: User Aggression and Fact Detection from the social media
contents. In User Aggression detection, The aim is to identify the level of
aggression from the contents generated in the Social media and written in the
English, Devanagari Hindi and Romanized Hindi. Aggression levels are
categorized into three predefined classes namely: `Non-aggressive`, `Overtly
Aggressive`, and `Covertly Aggressive`. During the disaster-related incident,
Social media like, Twitter is flooded with millions of posts. In such emergency
situations, identification of factual posts is important for organizations
involved in the relief operation. We anticipated this problem as a combination
of classification and Ranking problem. This paper presents a comparison of
various text representation scheme based on BoW techniques, distributed
word/sentence representation, transfer learning on classifiers. Weighted $F_1$
score is used as a primary evaluation metric. Results show that text
representation using BoW performs better than word embedding on machine
learning classifiers. While pre-trained Word embedding techniques perform
better on classifiers based on deep neural net. Recent transfer learning model
like ELMO, ULMFiT are fine-tuned for the Aggression classification task.
However, results are not at par with pre-trained word embedding model. Overall,
word embedding using fastText produce best weighted $F_1$-score than Word2Vec
and Glove. Results are further improved using pre-trained vector model.
Statistical significance tests are employed to ensure the significance of the
classification results. In the case of lexically different test Dataset, other
than training Dataset, deep neural models are more robust and perform
substantially better than machine learning classifiers.
SOMOSPIE: A modular SOil MOisture SPatial Inference Engine based on data driven decisions
Authors: Danny Rorabaugh, Mario Guevara, Ricardo Llamas, Joy Kitson, Rodrigo Vargas, Michela Taufer
Date: 2019-04-16 00:00:00
Tasks: BIG-bench Machine Learning, Management

The current availability of soil moisture data over large areas comes from satellite remote sensing technologies (i.e., radar-based systems), but these data have coarse resolution and often exhibit large spatial information gaps. Where data are too coarse or sparse for a given need (e.g., precision agriculture), one can leverage machine-learning techniques coupled with other sources of environmental information (e.g., topography) to generate gap-free information and at a finer spatial resolution (i.e., increased granularity). To this end, we develop a spatial inference engine consisting of modular stages for processing spatial environmental data, generating predictions with machine-learning techniques, and analyzing these predictions. We demonstrate the functionality of this approach and the effects of data processing choices via multiple prediction maps over a United States ecological region with a highly diverse soil moisture profile (i.e., the Middle Atlantic Coastal Plains). The relevance of our work derives from a pressing need to improve the spatial representation of soil moisture for applications in environmental sciences (e.g., ecological niche modeling, carbon monitoring systems, and other Earth system models) and precision agriculture (e.g., optimizing irrigation practices and other land management decisions).
Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading
Authors: Jaakko Sahlsten, Joel Jaskari, Jyri Kivinen, Lauri Turunen, Esa Jaanio, Kustaa Hietala, Kimmo Kaski
Date: 2019-04-16 00:00:00
Tasks: Deep Learning

Diabetes is a globally prevalent disease that can cause visible microvascular
complications such as diabetic retinopathy and macular edema in the human eye
retina, the images of which are today used for manual disease screening. This
labor-intensive task could greatly benefit from automatic detection using deep
learning technique. Here we present a deep learning system that identifies
referable diabetic retinopathy comparably or better than presented in the
previous studies, although we use only a small fraction of images (<1/4) in
training but are aided with higher image resolutions. We also provide novel
results for five different screening and clinical grading systems for diabetic
retinopathy and macular edema classification, including results for accurately
classifying images according to clinical five-grade diabetic retinopathy and
four-grade diabetic macular edema scales. These results suggest, that a deep
learning system could increase the cost-effectiveness of screening while
attaining higher than recommended performance, and that the system could be
applied in clinical examinations requiring finer grading.