pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Learning of Eye Gaze Representation from the Web
Authors: Neeru Dubey, Shreya Ghosh, Abhinav Dhall
Date: 2019-04-04 00:00:00
Tasks: Gaze Estimation

Automatic eye gaze estimation has interested researchers for a while now. In
this paper, we propose an unsupervised learning based method for estimating the
eye gaze region. To train the proposed network "Ize-Net" in self-supervised
manner, we collect a large `in the wild' dataset containing 1,54,251 images
from the web. For the images in the database, we divide the gaze into three
regions based on an automatic technique based on pupil-centers localization and
then use a feature-based technique to determine the gaze region. The
performance is evaluated on the Tablet Gaze and CAVE datasets by fine-tuning
results of Ize-Net for the task of eye gaze estimation. The feature
representation learned is also used to train traditional machine learning
algorithms for eye gaze estimation. The results demonstrate that the proposed
method learns a rich data representation, which can be efficiently fine-tuned
for any eye gaze estimation dataset.
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification
Authors: Cheng Deng, Yumeng Xue, Xianglong Liu, Chao Li, DaCheng Tao
Date: 2019-04-04 00:00:00
Tasks: Active Learning, General Classification, Hyperspectral Image Classification, image-classification, Image Classification, Transfer Learning

Deep learning has recently attracted significant attention in the field of
hyperspectral images (HSIs) classification. However, the construction of an
efficient deep neural network (DNN) mostly relies on a large number of labeled
samples being available. To address this problem, this paper proposes a unified
deep network, combined with active transfer learning that can be well-trained
for HSIs classification using only minimally labeled training data. More
specifically, deep joint spectral-spatial feature is first extracted through
hierarchical stacked sparse autoencoder (SSAE) networks. Active transfer
learning is then exploited to transfer the pre-trained SSAE network and the
limited training samples from the source domain to the target domain, where the
SSAE network is subsequently fine-tuned using the limited labeled samples
selected from both source and target domain by corresponding active learning
strategies. The advantages of our proposed method are threefold: 1) the network
can be effectively trained using only limited labeled samples with the help of
novel active learning strategies; 2) the network is flexible and scalable
enough to function across various transfer situations, including cross-dataset
and intra-image; 3) the learned deep joint spectral-spatial feature
representation is more generic and robust than many joint spectral-spatial
feature representation. Extensive comparative evaluations demonstrate that our
proposed method significantly outperforms many state-of-the-art approaches,
including both traditional and deep network-based methods, on three popular
datasets.
Triplet-Based Deep Hashing Network for Cross-Modal Retrieval
Authors: Cheng Deng, Zhaojia Chen, Xianglong Liu, Xinbo Gao, DaCheng Tao
Date: 2019-04-04 00:00:00
Tasks: Cross-Modal Retrieval, Deep Hashing, Retrieval, Semantic Similarity, Semantic Textual Similarity, Triplet

Given the benefits of its low storage requirements and high retrieval
efficiency, hashing has recently received increasing attention. In
particular,cross-modal hashing has been widely and successfully used in
multimedia similarity search applications. However, almost all existing methods
employing cross-modal hashing cannot obtain powerful hash codes due to their
ignoring the relative similarity between heterogeneous data that contains
richer semantic information, leading to unsatisfactory retrieval performance.
In this paper, we propose a triplet-based deep hashing (TDH) network for
cross-modal retrieval. First, we utilize the triplet labels, which describes
the relative relationships among three instances as supervision in order to
capture more general semantic correlations between cross-modal instances. We
then establish a loss function from the inter-modal view and the intra-modal
view to boost the discriminative abilities of the hash codes. Finally, graph
regularization is introduced into our proposed TDH method to preserve the
original semantic similarity between hash codes in Hamming space. Experimental
results show that our proposed method outperforms several state-of-the-art
approaches on two popular cross-modal datasets.
Malware Detection using Machine Learning and Deep Learning
Authors: Hemant Rathore, Swati Agarwal, Sanjay K. Sahay, Mohit Sewak
Date: 2019-04-04 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Malware Analysis, Malware Classification, Malware Detection

Research shows that over the last decade, malware has been growing
exponentially, causing substantial financial losses to various organizations.
Different anti-malware companies have been proposing solutions to defend
attacks from these malware. The velocity, volume, and the complexity of malware
are posing new challenges to the anti-malware community. Current
state-of-the-art research shows that recently, researchers and anti-virus
organizations started applying machine learning and deep learning methods for
malware analysis and detection. We have used opcode frequency as a feature
vector and applied unsupervised learning in addition to supervised learning for
malware classification. The focus of this tutorial is to present our work on
detecting malware with 1) various machine learning algorithms and 2) deep
learning models. Our results show that the Random Forest outperforms Deep
Neural Network with opcode frequency as a feature. Also in feature reduction,
Deep Auto-Encoders are overkill for the dataset, and elementary function like
Variance Threshold perform better than others. In addition to the proposed
methodologies, we will also discuss the additional issues and the unique
challenges in the domain, open research problems, limitations, and future
directions.
Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks
Authors: Richard McKinley, Michael Rebsamen, Raphael Meier, Mauricio Reyes, Christian Rummel, Roland Wiest
Date: 2019-04-04 00:00:00
Tasks: Anatomy, Brain Segmentation, Data Augmentation, Image Segmentation, Medical Image Segmentation, Semantic Segmentation

In applications of supervised learning applied to medical image segmentation,
the need for large amounts of labeled data typically goes unquestioned. In
particular, in the case of brain anatomy segmentation, hundreds or thousands of
weakly-labeled volumes are often used as training data. In this paper, we first
observe that for many brain structures, a small number of training examples,
(n=9), weakly labeled using Freesurfer 6.0, plus simple data augmentation,
suffice as training data to achieve high performance, achieving an overall mean
Dice coefficient of $0.84 \pm 0.12$ compared to Freesurfer over 28 brain
structures in T1-weighted images of $\approx 4000$ 9-10 year-olds from the
Adolescent Brain Cognitive Development study. We then examine two varieties of
heteroscedastic network as a method for improving classification results. An
existing proposal by Kendall and Gal, which uses Monte-Carlo inference to learn
to predict the variance of each prediction, yields an overall mean Dice of
$0.85 \pm 0.14$ and showed statistically significant improvements over 25 brain
structures. Meanwhile a novel heteroscedastic network which directly learns the
probability that an example has been mislabeled yielded an overall mean Dice of
$0.87 \pm 0.11$ and showed statistically significant improvements over all but
one of the brain structures considered. The loss function associated to this
network can be interpreted as performing a form of learned label smoothing,
where labels are only smoothed where they are judged to be uncertain.
Self-Adapting Goals Allow Transfer of Predictive Models to New Tasks
Authors: Kai Olav Ellefsen, Jim Torresen
Date: 2019-04-04 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A long-standing challenge in Reinforcement Learning is enabling agents to learn a model of their environment which can be transferred to solve other problems in a world with the same underlying rules. One reason this is difficult is the challenge of learning accurate models of an environment. If such a model is inaccurate, the agent's plans and actions will likely be sub-optimal, and likely lead to the wrong outcomes. Recent progress in model-based reinforcement learning has improved the ability for agents to learn and use predictive models. In this paper, we extend a recent deep learning architecture which learns a predictive model of the environment that aims to predict only the value of a few key measurements, which are be indicative of an agent's performance. Predicting only a few measurements rather than the entire future state of an environment makes it more feasible to learn a valuable predictive model. We extend this predictive model with a small, evolving neural network that suggests the best goals to pursue in the current state. We demonstrate that this allows the predictive model to transfer to new scenarios where goals are different, and that the adaptive goals can even adjust agent behavior on-line, changing its strategy to fit the current context.
Efficient GAN-based method for cyber-intrusion detection
Authors: Hongyu Chen, Li Jiang
Date: 2019-04-04 00:00:00
Tasks: Anomaly Detection, Generative Adversarial Network, Intrusion Detection

Ubiquitous anomalies endanger the security of our system constantly. They may bring irreversible damages to the system and cause leakage of privacy. Thus, it is of vital importance to promptly detect these anomalies. Traditional supervised methods such as Decision Trees and Support Vector Machine (SVM) are used to classify normality and abnormality. However, in some case the abnormal status are largely rarer than normal status, which leads to decision bias of these methods. Generative adversarial network (GAN) has been proposed to handle the case. With its strong generative ability, it only needs to learn the distribution of normal status, and identify the abnormal status through the gap between it and the learned distribution. Nevertheless, existing GAN-based models are not suitable to process data with discrete values, leading to immense degradation of detection performance. To cope with the discrete features, in this paper, we propose an efficient GAN-based model with specifically-designed loss function. Experiment results show that our model outperforms state-of-the-art models on discrete dataset and remarkably reduce the overhead.
Resource Efficient 3D Convolutional Neural Networks
Authors: Okan Köpüklü, Neslihan Kose, Ahmet Gunduz, Gerhard Rigoll
Date: 2019-04-04 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, GPU, Transfer Learning

Recently, convolutional neural networks with 3D kernels (3D CNNs) have been very popular in computer vision community as a result of their superior ability of extracting spatio-temporal features within video frames compared to 2D CNNs. Although there has been great advances recently to build resource efficient 2D CNN architectures considering memory and power budget, there is hardly any similar resource efficient architectures for 3D CNNs. In this paper, we have converted various well-known resource efficient 2D CNNs to 3D CNNs and evaluated their performance on three major benchmarks in terms of classification accuracy for different complexity levels. We have experimented on (1) Kinetics-600 dataset to inspect their capacity to learn, (2) Jester dataset to inspect their ability to capture motion patterns, and (3) UCF-101 to inspect the applicability of transfer learning. We have evaluated the run-time performance of each model on a single Titan XP GPU and a Jetson TX2 embedded system. The results of this study show that these models can be utilized for different types of real-world applications since they provide real-time performance with considerable accuracies and memory usage. Our analysis on different complexity levels shows that the resource efficient 3D CNNs should not be designed too shallow or narrow in order to save complexity. The codes and pretrained models used in this work are publicly available.
Transfer Learning with Sparse Associative Memories
Authors: Quentin Jodelet, Vincent Gripon, Masafumi Hagiwara
Date: 2019-04-04 00:00:00
Tasks: General Classification, Incremental Learning, Transfer Learning

In this paper, we introduce a novel layer designed to be used as the output of pre-trained neural networks in the context of classification. Based on Associative Memories, this layer can help design Deep Neural Networks which support incremental learning and that can be (partially) trained in real time on embedded devices. Experiments on the ImageNet dataset and other different domain specific datasets show that it is possible to design more flexible and faster-to-train Neural Networks at the cost of a slight decrease in accuracy.
Learning to Decipher Hate Symbols
Authors: Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: General Classification

Existing computational models to understand hate speech typically frame the
problem as a simple classification task, bypassing the understanding of hate
symbols (e.g., 14 words, kigy) and their secret connotations. In this paper, we
propose a novel task of deciphering hate symbols. To do this, we leverage the
Urban Dictionary and collected a new, symbol-rich Twitter corpus of hate
speech. We investigate neural network latent context models for deciphering
hate symbols. More specifically, we study Sequence-to-Sequence models and show
how they are able to crack the ciphers based on context. Furthermore, we
propose a novel Variational Decipher and show how it can generalize better to
unseen hate symbols in a more challenging testing setting.
White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks
Authors: Yotam Gil, Yoav Chai, Or Gorodissky, Jonathan Berant
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Efficient Neural Network

Adversarial examples are important for understanding the behavior of neural
models, and can improve their robustness through adversarial training. Recent
work in natural language processing generated adversarial examples by assuming
white-box access to the attacked model, and optimizing the input directly
against it (Ebrahimi et al., 2018). In this work, we show that the knowledge
implicit in the optimization procedure can be distilled into another more
efficient neural network. We train a model to emulate the behavior of a
white-box attack and show that it generalizes well across examples. Moreover,
it reduces adversarial example generation time by 19x-39x. We also show that
our approach transfers to a black-box setting, by attacking The Google
Perspective API and exposing its vulnerability. Our attack flips the
API-predicted label in 42\% of the generated examples, while humans maintain
high-accuracy in predicting the gold label.
Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling
Authors: Prince Zizhuang Wang, William Yang Wang
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Modeling, Language Modelling, Text Generation

Recurrent Variational Autoencoder has been widely used for language modeling
and text generation tasks. These models often face a difficult optimization
problem, also known as the Kullback-Leibler (KL) term vanishing issue, where
the posterior easily collapses to the prior, and the model will ignore latent
codes in generative tasks. To address this problem, we introduce an improved
Wasserstein Variational Autoencoder (WAE) with Riemannian Normalizing Flow
(RNF) for text modeling. The RNF transforms a latent variable into a space that
respects the geometric characteristics of input space, which makes posterior
impossible to collapse to the non-informative prior. The Wasserstein objective
minimizes the distance between the marginal distribution and the prior directly
and therefore does not force the posterior to match the prior. Empirical
experiments show that our model avoids KL vanishing over a range of datasets
and has better performances in tasks such as language modeling, likelihood
approximation, and text generation. Through a series of experiments and
analysis over latent space, we show that our model learns latent distributions
that respect latent space geometry and is able to generate sentences that are
more diverse.
Convergence analysis of beetle antennae search algorithm and its applications
Authors: Yinyan Zhang, Shuai Li, Bin Xu
Date: 2019-04-04 00:00:00
Tasks: global-optimization

The beetle antennae search algorithm was recently proposed and investigated
for solving global optimization problems. Although the performance of the
algorithm and its variants were shown to be better than some existing
meta-heuristic algorithms, there is still a lack of convergence analysis. In
this paper, we provide theoretical analysis on the convergence of the beetle
antennae search algorithm. We test the performance of the BAS algorithm via
some representative benchmark functions. Meanwhile, some applications of the
BAS algorithm are also presented.
Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning
Authors: Jiachen Li, Hengbo Ma, Masayoshi Tomizuka
Date: 2019-04-04 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Generative Adversarial Network, Motion Planning, State Estimation

In order to enable high-quality decision making and motion planning of
intelligent systems such as robotics and autonomous vehicles, accurate
probabilistic predictions for surrounding interactive objects is a crucial
prerequisite. Although many research studies have been devoted to making
predictions on a single entity, it remains an open challenge to forecast future
behaviors for multiple interactive agents simultaneously. In this work, we take
advantage of the Generative Adversarial Network (GAN) due to its capability of
distribution learning and propose a generic multi-agent probabilistic
prediction and tracking framework which takes the interactions among multiple
entities into account, in which all the entities are treated as a whole.
However, since GAN is very hard to train, we make an empirical research and
present the relationship between training performance and hyperparameter values
with a numerical case study. The results imply that the proposed model can
capture both the mean, variance and multi-modalities of the groundtruth
distribution. Moreover, we apply the proposed approach to a real-world task of
vehicle behavior prediction to demonstrate its effectiveness and accuracy. The
results illustrate that the proposed model trained by adversarial learning can
achieve a better prediction performance than other state-of-the-art models
trained by traditional supervised learning which maximizes the data likelihood.
The well-trained model can also be utilized as an implicit proposal
distribution for particle filtered based Bayesian state estimation.
Artificial Neural Network Modeling for Path Loss Prediction in Urban Environments
Authors: Chanshin Park, Daniel K. Tettey, Han-Shin Jo
Date: 2019-04-04 00:00:00
Tasks: regression

Although various linear log-distance path loss models have been developed,
advanced models are requiring to more accurately and flexibly represent the
path loss for complex environments such as the urban area. This letter proposes
an artificial neural network (ANN) based multi-dimensional regression framework
for path loss modeling in urban environments at 3 to 6 GHz frequency band. ANN
is used to learn the path loss structure from the measured path loss data which
is a function of distance and frequency. The effect of the network architecture
parameter (activation function, the number of hidden layers and nodes) on the
prediction accuracy are analyzed. We observe that the proposed model is more
accurate and flexible compared to the conventional linear model.
Inferring Dynamic Representations of Facial Actions from a Still Image
Authors: Siyang Song, Enrique Sánchez-Lozano, Linlin Shen, Alan Johnston, Michel Valstar
Date: 2019-04-04 00:00:00

Facial actions are spatio-temporal signals by nature, and therefore their
modeling is crucially dependent on the availability of temporal information. In
this paper, we focus on inferring such temporal dynamics of facial actions when
no explicit temporal information is available, i.e. from still images. We
present a novel approach to capture multiple scales of such temporal dynamics,
with an application to facial Action Unit (AU) intensity estimation and
dimensional affect estimation. In particular, 1) we propose a framework that
infers a dynamic representation (DR) from a still image, which captures the
bi-directional flow of time within a short time-window centered at the input
image; 2) we show that we can train our method without the need of explicitly
generating target representations, allowing the network to represent dynamics
more broadly; and 3) we propose to apply a multiple temporal scale approach
that infers DRs for different window lengths (MDR) from a still image. We
empirically validate the value of our approach on the task of frame ranking,
and show how our proposed MDR attains state of the art results on BP4D for AU
intensity estimation and on SEMAINE for dimensional affect estimation, using
only still images at test time.
ConvPoint: Continuous Convolutions for Point Cloud Processing
Authors: Alexandre Boulch
Date: 2019-04-04 00:00:00
Tasks: 3D Part Segmentation, 3D Semantic Segmentation, General Classification, LIDAR Semantic Segmentation, Segmentation, Semantic Segmentation

Point clouds are unstructured and unordered data, as opposed to images. Thus, most machine learning approach developed for image cannot be directly transferred to point clouds. In this paper, we propose a generalization of discrete convolutional neural networks (CNNs) in order to deal with point clouds by replacing discrete kernels by continuous ones. This formulation is simple, allows arbitrary point cloud sizes and can easily be used for designing neural networks similarly to 2D CNNs. We present experimental results with various architectures, highlighting the flexibility of the proposed approach. We obtain competitive results compared to the state-of-the-art on shape classification, part segmentation and semantic segmentation for large-scale point clouds.
Multi-reference Tacotron by Intercross Training for Style Disentangling,Transfer and Control in Speech Synthesis
Authors: Yanyao Bian, Changbin Chen, Yongguo Kang, Zhenglin Pan
Date: 2019-04-04 00:00:00
Tasks: Diversity, Speech Synthesis

Speech style control and transfer techniques aim to enrich the diversity and
expressiveness of synthesized speech. Existing approaches model all speech
styles into one representation, lacking the ability to control a specific
speech feature independently. To address this issue, we introduce a novel
multi-reference structure to Tacotron and propose intercross training approach,
which together ensure that each sub-encoder of the multi-reference encoder
independently disentangles and controls a specific style. Experimental results
show that our model is able to control and transfer desired speech styles
individually.