pwc-archive/papers-with-abstracts huggingface dataset:
Country-wide high-resolution vegetation height mapping with Sentinel-2
Authors: Nico Lang, Konrad Schindler, Jan Dirk Wegner
Date: 2019-04-30 00:00:00
Tasks: Surface Reconstruction, Vocal Bursts Intensity Prediction

Sentinel-2 multi-spectral images collected over periods of several months were used to estimate vegetation height for Gabon and Switzerland. A deep convolutional neural network (CNN) was trained to extract suitable spectral and textural features from reflectance images and to regress per-pixel vegetation height. In Gabon, reference heights for training and validation were derived from airborne LiDAR measurements. In Switzerland, reference heights were taken from an existing canopy height model derived via photogrammetric surface reconstruction. The resulting maps have a mean absolute error (MAE) of 1.7 m in Switzerland and 4.3 m in Gabon (a root mean square error (RMSE) of 3.4 m and 5.6 m, respectively), and correctly estimate vegetation heights up to >50 m. They also show good qualitative agreement with existing vegetation height maps. Our work demonstrates that, given a moderate amount of reference data (i.e., 2000 km$^2$ in Gabon and $\approx$5800 km$^2$ in Switzerland), high-resolution vegetation height maps with 10 m ground sampling distance (GSD) can be derived at country scale from Sentinel-2 imagery.
Handwritten Chinese Font Generation with Collaborative Stroke Refinement
Authors: Chuan Wen, Jie Chang, Ya zhang, Siheng Chen, Yan-Feng Wang, Mei Han, Qi Tian
Date: 2019-04-30 00:00:00
Tasks: Font Generation

Automatic character generation is an appealing solution for new typeface design, especially for Chinese typefaces including over 3700 most commonly-used characters. This task has two main pain points: (i) handwritten characters are usually associated with thin strokes of few information and complex structure which are error prone during deformation; (ii) thousands of characters with various shapes are needed to synthesize based on a few manually designed characters. To solve those issues, we propose a novel convolutional-neural-network-based model with three main techniques: collaborative stroke refinement, using collaborative training strategy to recover the missing or broken strokes; online zoom-augmentation, taking the advantage of the content-reuse phenomenon to reduce the size of training set; and adaptive pre-deformation, standardizing and aligning the characters. The proposed model needs only 750 paired training samples; no pre-trained network, extra dataset resource or labels is needed. Experimental results show that the proposed method significantly outperforms the state-of-the-art methods under the practical restriction on handwritten font synthesis.
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
Authors: Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Francesco Moramarco, Jack Flann, Nils Y. Hammerla
Date: 2019-04-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: Semantic Textual Similarity, Sentence, STS, Word Embeddings

Recent literature suggests that averaged word vectors followed by simple
post-processing outperform many deep learning methods on semantic textual
similarity tasks. Furthermore, when averaged word vectors are trained
supervised on large corpora of paraphrases, they achieve state-of-the-art
results on standard STS benchmarks. Inspired by these insights, we push the
limits of word embeddings even further. We propose a novel fuzzy bag-of-words
(FBoW) representation for text that contains all the words in the vocabulary
simultaneously but with different degrees of membership, which are derived from
similarities between word vectors. We show that max-pooled word vectors are
only a special case of fuzzy BoW and should be compared via fuzzy Jaccard index
rather than cosine similarity. Finally, we propose DynaMax, a completely
unsupervised and non-parametric similarity measure that dynamically extracts
and max-pools good features depending on the sentence pair. This method is both
efficient and easy to implement, yet outperforms current baselines on STS tasks
by a large margin and is even competitive with supervised word vectors trained
to directly optimise cosine similarity.
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks
Authors: Gauthier Gidel, Francis Bach, Simon Lacoste-Julien
Date: 2019-04-30 00:00:00
Proceeding: NeurIPS 2019 12

When optimizing over-parameterized models, such as deep neural networks, a large set of parameters can achieve zero training error. In such cases, the choice of the optimization algorithm and its respective hyper-parameters introduces biases that will lead to convergence to specific minimizers of the objective. Consequently, this choice can be considered as an implicit regularization for the training of over-parametrized models. In this work, we push this idea further by studying the discrete gradient dynamics of the training of a two-layer linear network with the least-squares loss. Using a time rescaling, we show that, with a vanishing initialization and a small enough step size, this dynamics sequentially learns the solutions of a reduced-rank regression with a gradually increasing rank.
English Broadcast News Speech Recognition by Humans and Machines
Authors: Samuel Thomas, Masayuki Suzuki, Yinghui Huang, Gakuto Kurata, Zoltan Tuske, George Saon, Brian Kingsbury, Michael Picheny, Tom Dibert, Alice Kaiser-Schatzlein, Bern Samko
Date: 2019-04-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

With recent advances in deep learning, considerable attention has been given
to achieving automatic speech recognition performance close to human
performance on tasks like conversational telephone speech (CTS) recognition. In
this paper we evaluate the usefulness of these proposed techniques on broadcast
news (BN), a similar challenging task. We also perform a set of recognition
measurements to understand how close the achieved automatic speech recognition
results are to human performance on this task. On two publicly available BN
test sets, DEV04F and RT04, our speech recognition system using LSTM and
residual network based acoustic models with a combination of n-gram and neural
network language models performs at 6.5% and 5.9% word error rate. By achieving
new performance milestones on these test sets, our experiments show that
techniques developed on other related tasks, like CTS, can be transferred to
achieve similar performance. In contrast, the best measured human recognition
performance on these test sets is much lower, at 3.6% and 2.8% respectively,
indicating that there is still room for new techniques and improvements in this
space, to reach human performance levels.
Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning
Authors: Kacper Kielak
Date: 2019-04-30 00:00:00
Tasks: Deep Reinforcement Learning, Game of Go, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning has seen great advancements in the past five years. The successful introduction of deep learning in place of more traditional methods allowed reinforcement learning to scale to very complex domains achieving super-human performance in environments like the game of Go or numerous video games. Despite great successes in multiple domains, these new methods suffer from their own issues that make them often inapplicable to the real world problems. Extreme lack of data efficiency, together with huge variance and difficulty in enforcing safety constraints, is one of the three most prominent issues in the field. Usually, millions of data points sampled from the environment are necessary for these algorithms to converge to acceptable policies. This thesis proposes novel Generative Adversarial Imaginative Reinforcement Learning algorithm. It takes advantage of the recent introduction of highly effective generative adversarial models, and Markov property that underpins reinforcement learning setting, to model dynamics of the real environment within the internal imagination module. Rollouts from the imagination are then used to artificially simulate the real environment in a standard reinforcement learning process to avoid, often expensive and dangerous, trial and error in the real environment. Experimental results show that the proposed algorithm more economically utilises experience from the real environment than the current state-of-the-art Rainbow DQN algorithm, and thus makes an important step towards sample efficient deep reinforcement learning.
Online Causal Structure Learning in the Presence of Latent Variables
Authors: Durdane Kocacoban, James Cussens
Date: 2019-04-30 00:00:00

We present two online causal structure learning algorithms which can track changes in a causal structure and process data in a dynamic real-time manner. Standard causal structure learning algorithms assume that causal structure does not change during the data collection process, but in real-world scenarios, it does often change. Therefore, it is inappropriate to handle such changes with existing batch-learning approaches, and instead, a structure should be learned in an online manner. The online causal structure learning algorithms we present here can revise correlation values without reprocessing the entire dataset and use an existing model to avoid relearning the causal links in the prior model, which still fit data. Proposed algorithms are tested on synthetic and real-world datasets, the latter being a seasonally adjusted commodity price index dataset for the U.S. The online causal structure learning algorithms outperformed standard FCI by a large margin in learning the changed causal structure correctly and efficiently when latent variables were present.
Automatic Emotion Recognition (AER) System based on Two-Level Ensemble of Lightweight Deep CNN Models
Authors: Emad-ul-Haq Qazi, Muhammad Hussain, Hatim AboAlsamh, Ihsan Ullah
Date: 2019-04-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition

Emotions play a crucial role in human interaction, health care and security
investigations and monitoring. Automatic emotion recognition (AER) using
electroencephalogram (EEG) signals is an effective method for decoding the real
emotions, which are independent of body gestures, but it is a challenging
problem. Several automatic emotion recognition systems have been proposed,
which are based on traditional hand-engineered approaches and their
performances are very poor. Motivated by the outstanding performance of deep
learning (DL) in many recognition tasks, we introduce an AER system (Deep-AER)
based on EEG brain signals using DL. A DL model involves a large number of
learnable parameters, and its training needs a large dataset of EEG signals,
which is difficult to acquire for AER problem. To overcome this problem, we
proposed a lightweight pyramidal one-dimensional convolutional neural network
(LP-1D-CNN) model, which involves a small number of learnable parameters. Using
LP-1D-CNN, we build a two level ensemble model. In the first level of the
ensemble, each channel is scanned incrementally by LP-1D-CNN to generate
predictions, which are fused using majority vote. The second level of the
ensemble combines the predictions of all channels of an EEG signal using
majority vote for detecting the emotion state. We validated the effectiveness
and robustness of Deep-AER using DEAP, a benchmark dataset for emotion
recognition research. The results indicate that FRONT plays dominant role in
AER and over this region, Deep-AER achieved the accuracies of 98.43% and 97.65%
for two AER problems, i.e., high valence vs low valence (HV vs LV) and high
arousal vs low arousal (HA vs LA), respectively. The comparison reveals that
Deep-AER outperforms the state-of-the-art systems with large margin. The
Deep-AER system will be helpful in monitoring for health care and security
investigations.
An Efficient Intelligent System for the Classification of Electroencephalography (EEG) Brain Signals using Nuclear Features for Human Cognitive Tasks
Authors: Emad-ul-Haq Qazi, Muhammad Hussain, Hatim AboAlSamh
Date: 2019-04-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification

Representation and classification of Electroencephalography (EEG) brain
signals are critical processes for their analysis in cognitive tasks.
Particularly, extraction of discriminative features from raw EEG signals,
without any pre-processing, is a challenging task. Motivated by nuclear norm,
we observed that there is a significant difference between the variances of EEG
signals captured from the same brain region when a subject performs different
tasks. This observation lead us to use singular value decomposition for
computing dominant variances of EEG signals captured from a certain brain
region while performing a certain task and use them as features (nuclear
features). A simple and efficient class means based minimum distance classifier
(CMMDC) is enough to predict brain states. This approach results in the feature
space of significantly small dimension and gives equally good classification
results on clean as well as raw data. We validated the effectiveness and
robustness of the technique using four datasets of different tasks: fluid
intelligence clean data (FICD), fluid intelligence raw data (FIRD), memory
recall task (MRT), and eyes open / eyes closed task (EOEC). For each task, we
analyzed EEG signals over six (06) different brain regions with 8, 16, 20, 18,
18 and 100 electrodes. The nuclear features from frontal brain region gave the
100% prediction accuracy. The discriminant analysis of the nuclear features has
been conducted using intra-class and inter-class variations. Comparisons with
the state-of-the-art techniques showed the superiority of the proposed system.
Eigen Values Features for the Classification of Brain Signals corresponding to 2D and 3D Educational Contents
Authors: Saeed Bamatraf, Muhammad Hussain, Emad-ul-Haq Qazi, Hatim AboAlSamh
Date: 2019-04-30 00:00:00
Tasks: General Classification, Multiple-choice

In this paper, we have proposed a brain signal classification method, which
uses eigenvalues of the covariance matrix as features to classify images
(topomaps) created from the brain signals. The signals are recorded during the
answering of 2D and 3D questions. The system is used to classify the correct
and incorrect answers for both 2D and 3D questions. Using the classification
technique, the impacts of 2D and 3D multimedia educational contents on
learning, memory retention and recall will be compared. The subjects learn
similar 2D and 3D educational contents. Afterwards, subjects are asked 20
multiple-choice questions (MCQs) associated with the contents after thirty
minutes (Short-Term Memory) and two months (Long-Term Memory). Eigenvalues
features extracted from topomaps images are given to K-Nearest Neighbor (KNN)
and Support Vector Machine (SVM) classifiers, in order to identify the states
of the brain related to incorrect and correct answers. Excellent accuracies
obtained by both classifiers and by applying statistical analysis on the
results, no significant difference is indicated between 2D and 3D multimedia
educational contents on learning, memory retention and recall in both STM and
LTM.
GaborNet: Gabor filters with learnable parameters in deep convolutional neural networks
Authors: Andrey Alekseev, Anatoly Bobe
Date: 2019-04-30 00:00:00

The article describes a system for image recognition using deep convolutional
neural networks. Modified network architecture is proposed that focuses on
improving convergence and reducing training complexity. The filters in the
first layer of the network are constrained to fit the Gabor function. The
parameters of Gabor functions are learnable and are updated by standard
backpropagation techniques. The system was implemented on Python, tested on
several datasets and outperformed the common convolutional networks.
Investigation of Initialization Strategies for the Multiple Instance Adaptive Cosine Estimator
Authors: James Bocinsky, Connor McCurley, Daniel Shats, Alina Zare
Date: 2019-04-30 00:00:00

Sensors which use electromagnetic induction (EMI) to excite a response in
conducting bodies have long been investigated for subsurface explosive hazard
detection. In particular, EMI sensors have been used to discriminate between
different types of objects, and to detect objects with low metal content. One
successful, previously investigated approach is the Multiple Instance Adaptive
Cosine Estimator (MI-ACE). In this paper, a number of new initialization
techniques for MI-ACE are proposed and evaluated using their respective
performance and speed. The cross validated learned signatures, as well as
learned background statistics, are used with Adaptive Cosine Estimator (ACE) to
generate confidence maps, which are clustered into alarms. Alarms are scored
against a ground truth and the initialization approaches are compared.
Semantic Referee: A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation
Authors: Marjan Alirezaie, Martin Längkvist, Michael Sioutis, Amy Loutfi
Date: 2019-04-30 00:00:00
Tasks: BIG-bench Machine Learning, Semantic Segmentation

Understanding why machine learning algorithms may fail is usually the task of
the human expert that uses domain knowledge and contextual information to
discover systematic shortcomings in either the data or the algorithm. In this
paper, we propose a semantic referee, which is able to extract qualitative
features of the errors emerging from deep machine learning frameworks and
suggest corrections. The semantic referee relies on ontological reasoning about
spatial knowledge in order to characterize errors in terms of their spatial
relations with the environment. Using semantics, the reasoner interacts with
the learning algorithm as a supervisor. In this paper, the proposed method of
the interaction between a neural network classifier and a semantic referee
shows how to improve the performance of semantic segmentation for satellite
imagery data.
Test Selection for Deep Learning Systems
Authors: Wei Ma, Mike Papadakis, Anestis Tsakmalis, Maxime Cordy, Yves Le Traon
Date: 2019-04-30 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification, software testing

Testing of deep learning models is challenging due to the excessive number
and complexity of computations involved. As a result, test data selection is
performed manually and in an ad hoc way. This raises the question of how we can
automatically select candidate test data to test deep learning models. Recent
research has focused on adapting test selection metrics from code-based
software testing (such as coverage) to deep learning. However, deep learning
models have different attributes from code such as spread of computations
across the entire network reflecting training data properties, balance of
neuron weights and redundancy (use of many more neurons than needed). Such
differences make code-based metrics inappropriate to select data that can
challenge the models (can trigger misclassification). We thus propose a set of
test selection metrics based on the notion of model uncertainty (model
confidence on specific inputs). Intuitively, the more uncertain we are about a
candidate sample, the more likely it is that this sample triggers a
misclassification. Similarly, the samples for which we are the most uncertain,
are the most informative and should be used to improve the model by retraining.
We evaluate these metrics on two widely-used image classification problems
involving real and artificial (adversarial) data. We show that
uncertainty-based metrics have a strong ability to select data that are
misclassified and lead to major improvement in classification accuracy during
retraining: up to 80% more gain than random selection and other
state-of-the-art metrics on one dataset and up to 29% on the other.
Using cameras for precise measurement of two-dimensional plant features: CASS
Authors: Amy Tabb, Germán A Holguín, Rachel Naegele
Date: 2019-04-30 00:00:00
Tasks: Plant Phenotyping, Vocal Bursts Valence Prediction

Images are used frequently in plant phenotyping to capture measurements. This chapter offers a repeatable method for capturing two-dimensional measurements of plant parts in field or laboratory settings using a variety of camera styles (cellular phone, DSLR), with the addition of a printed calibration pattern. The method is based on calibrating the camera using information available from the EXIF tags from the image, as well as visual information from the pattern. Code is provided to implement the method, as well as a dataset for testing. We include steps to verify protocol correctness by imaging an artifact. The use of this protocol for two-dimensional plant phenotyping will allow data capture from different cameras and environments, with comparison on the same physical scale. We abbreviate this method as CASS, for CAmera aS Scanner. Code and data is available at http://doi.org/10.5281/zenodo.3677473.
Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration
Authors: Shuhan Tan, Jiening Jiao, Wei-Shi Zheng
Date: 2019-04-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Transfer Learning

In conventional domain adaptation, a critical assumption is that there exists
a fully labeled domain (source) that contains the same label space as another
unlabeled or scarcely labeled domain (target). However, in the real world,
there often exist application scenarios in which both domains are partially
labeled and not all classes are shared between these two domains. Thus, it is
meaningful to let partially labeled domains learn from each other to classify
all the unlabeled samples in each domain under an open-set setting. We consider
this problem as weakly supervised open-set domain adaptation. To address this
practical setting, we propose the Collaborative Distribution Alignment (CDA)
method, which performs knowledge transfer bilaterally and works collaboratively
to classify unlabeled data and identify outlier samples. Extensive experiments
on the Office benchmark and an application on person reidentification show that
our method achieves state-of-the-art performance.
Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage
Authors: Abhishek Abhishek, Sanya Bathla Taneja, Garima Malik, Ashish Anand, Amit Awekar
Date: 2019-04-30 00:00:00
Proceeding: AKBC 2019
Tasks: Benchmarking

Fine-grained Entity Recognition (FgER) is the task of detecting and
classifying entity mentions to a large set of types spanning diverse domains
such as biomedical, finance and sports. We observe that when the type set spans
several domains, detection of entity mention becomes a limitation for
supervised learning models. The primary reason being lack of dataset where
entity boundaries are properly annotated while covering a large spectrum of
entity types. Our work directly addresses this issue. We propose Heuristics
Allied with Distant Supervision (HAnDS) framework to automatically construct a
quality dataset suitable for the FgER task. HAnDS framework exploits the high
interlink among Wikipedia and Freebase in a pipelined manner, reducing
annotation errors introduced by naively using distant supervision approach.
Using HAnDS framework, we create two datasets, one suitable for building FgER
systems recognizing up to 118 entity types based on the FIGER type hierarchy
and another for up to 1115 entity types based on the TypeNet hierarchy. Our
extensive empirical experimentation warrants the quality of the generated
datasets. Along with this, we also provide a manually annotated dataset for
benchmarking FgER systems.