pwc-archive/papers-with-abstracts huggingface dataset:
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
Authors: Umur Aybars Ciftci, Ilke Demir
Date: 2019-01-08 00:00:00
Tasks: Dimensionality Reduction, Video Compression

The recent proliferation of fake portrait videos poses direct threats on society, law, and privacy. Believing the fake video of a politician, distributing fake pornographic content of celebrities, fabricating impersonated fake videos as evidence in courts are just a few real world consequences of deep fakes. We present a novel approach to detect synthetic content in portrait videos, as a preventive solution for the emerging threat of deep fakes. In other words, we introduce a deep fake detector. We observe that detectors blindly utilizing deep learning are not effective in catching fake content, as generative models produce formidably realistic results. Our key assertion follows that biological signals hidden in portrait videos can be used as an implicit descriptor of authenticity, because they are neither spatially nor temporally preserved in fake content. To prove and exploit this assertion, we first engage several signal transformations for the pairwise separation problem, achieving 99.39% accuracy. Second, we utilize those findings to formulate a generalized classifier for fake content, by analyzing proposed signal transformations and corresponding feature sets. Third, we generate novel signal maps and employ a CNN to improve our traditional classifier for detecting synthetic content. Lastly, we release an "in the wild" dataset of fake portrait videos that we collected as a part of our evaluation process. We evaluate FakeCatcher on several datasets, resulting with 96%, 94.65%, 91.50%, and 91.07% accuracies, on Face Forensics, Face Forensics++, CelebDF, and on our new Deep Fakes Dataset respectively. We also analyze signals from various facial regions, under image distortions, with varying segment durations, from different generators, against unseen datasets, and under several dimensionality reduction techniques.
FIGR: Few-shot Image Generation with Reptile
Authors: Louis Clouâtre, Marc Demers
Date: 2019-01-08 00:00:00
Tasks: Few-Shot Learning, Image Generation, Meta-Learning

Generative Adversarial Networks (GAN) boast impressive capacity to generate
realistic images. However, like much of the field of deep learning, they
require an inordinate amount of data to produce results, thereby limiting their
usefulness in generating novelty. In the same vein, recent advances in
meta-learning have opened the door to many few-shot learning applications. In
the present work, we propose Few-shot Image Generation using Reptile (FIGR), a
GAN meta-trained with Reptile. Our model successfully generates novel images on
both MNIST and Omniglot with as little as 4 images from an unseen class. We
further contribute FIGR-8, a new dataset for few-shot image generation, which
contains 1,548,944 icons categorized in over 18,409 classes. Trained on FIGR-8,
initial results show that our model can generalize to more advanced concepts
(such as "bird" and "knife") from as few as 8 samples from a previously unseen
class of images and as little as 10 training steps through those 8 images. This
work demonstrates the potential of training a GAN for few-shot image generation
and aims to set a new benchmark for future work in the domain.
Data Masking with Privacy Guarantees
Authors: Anh T. Pham, Shalini Ghosh, Vinod Yegneswaran
Date: 2019-01-08 00:00:00

We study the problem of data release with privacy, where data is made
available with privacy guarantees while keeping the usability of the data as
high as possible --- this is important in health-care and other domains with
sensitive data. In particular, we propose a method of masking the private data
with privacy guarantee while ensuring that a classifier trained on the masked
data is similar to the classifier trained on the original data, to maintain
usability. We analyze the theoretical risks of the proposed method and the
traditional input perturbation method. Results show that the proposed method
achieves lower risk compared to the input perturbation, especially when the
number of training samples gets large. We illustrate the effectiveness of the
proposed method of data masking for privacy-sensitive learning on $12$
benchmark datasets.
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
Authors: Zenan Ling, Haotian Ma, Yu Yang, Robert C. Qiu, Song-Chun Zhu, Quanshi Zhang
Date: 2019-01-08 00:00:00

In this paper, we propose to disentangle and interpret contextual effects
that are encoded in a pre-trained deep neural network. We use our method to
explain the gaming strategy of the alphaGo Zero model. Unlike previous studies
that visualized image appearances corresponding to the network output or a
neural activation only from a global perspective, our research aims to clarify
how a certain input unit (dimension) collaborates with other units (dimensions)
to constitute inference patterns of the neural network and thus contribute to
the network output. The analysis of local contextual effects w.r.t. certain
input units is of special values in real applications. Explaining the logic of
the alphaGo Zero model is a typical application. In experiments, our method
successfully disentangled the rationale of each move during the Go game.
Comments on "Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?"
Authors: Talha Cihad Gulcu, Alper Gungor
Date: 2019-01-08 00:00:00
Tasks: General Classification

In a recently published paper [1], it is shown that deep neural networks
(DNNs) with random Gaussian weights preserve the metric structure of the data,
with the property that the distance shrinks more when the angle between the two
data points is smaller. We agree that the random projection setup considered in
[1] preserves distances with a high probability. But as far as we are
concerned, the relation between the angle of the data points and the output
distances is quite the opposite, i.e., smaller angles result in a weaker
distance shrinkage. This leads us to conclude that Theorem 3 and Figure 5 in
[1] are not accurate. Hence the usage of random Gaussian weights in DNNs cannot
provide an ability of universal classification or treating in-class and
out-of-class data separately. Consequently, the behavior of networks consisting
of random Gaussian weights only is not useful to explain how DNNs achieve
state-of-art results in a large variety of problems.
Solar-Sail Trajectory Design for Multiple Near Earth Asteroid Exploration Based on Deep Neural Networks
Authors: Yu Song, Shengping Gong
Date: 2019-01-08 00:00:00

In the preliminary trajectory design of the multi-target rendezvous problem,
a model that can quickly estimate the cost of the orbital transfer is
essential. The estimation of the transfer time using solar sail between two
arbitrary orbits is difficult and usually requires to solve an optimal control
problem. Inspired by the successful applications of the deep neural network in
nonlinear regression, this work explores the possibility and effectiveness of
mapping the transfer time for solar sail from the orbital characteristics using
the deep neural network. Furthermore, the Monte Carlo Tree Search method is
investigated and used to search the optimal sequence considering a
multi-asteroid exploration problem. The obtained sequences from preliminary
design will be solved and verified by sequentially solving the optimal control
problem. Two examples of different application backgrounds validate the
effectiveness of the proposed approach.
Risk-Aware Active Inverse Reinforcement Learning
Authors: Daniel S. Brown, Yuchen Cui, Scott Niekum
Date: 2019-01-08 00:00:00
Tasks: Active Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query strategies; however, to our knowledge, none of these strategies directly minimize the performance risk of the policy the robot is learning. Utilizing recent advances in performance bounds for inverse reinforcement learning, we propose a risk-aware active inverse reinforcement learning algorithm that focuses active queries on areas of the state space with the potential for large generalization error. We show that risk-aware active learning outperforms standard active IRL approaches on gridworld, simulated driving, and table setting tasks, while also providing a performance-based stopping criterion that allows a robot to know when it has received enough demonstrations to safely perform a task.
Ensembles of feedforward-designed convolutional neural networks
Authors: Yueru Chen, Yijing Yang, Wei Wang, C. -C. Jay Kuo
Date: 2019-01-08 00:00:00
Tasks: Diversity, General Classification, image-classification, Image Classification

An ensemble method that fuses the output decision vectors of multiple
feedforward-designed convolutional neural networks (FF-CNNs) to solve the image
classification problem is proposed in this work. To enhance the performance of
the ensemble system, it is critical to increasing the diversity of FF-CNN
models. To achieve this objective, we introduce diversities by adopting three
strategies: 1) different parameter settings in convolutional layers, 2)
flexible feature subsets fed into the Fully-connected (FC) layers, and 3)
multiple image embeddings of the same input source. Furthermore, we partition
input samples into easy and hard ones based on their decision confidence
scores. As a result, we can develop a new ensemble system tailored to hard
samples to further boost classification accuracy. Experiments are conducted on
the MNIST and CIFAR-10 datasets to demonstrate the effectiveness of the
ensemble method.
Audio Captcha Recognition Using RastaPLP Features by SVM
Authors: Ahmet Faruk Cakmak, Muhammet Balcilar
Date: 2019-01-08 00:00:00

Nowadays, CAPTCHAs are computer generated tests that human can pass but
current computer systems can not. They have common usage in various web
services in order to be able to detect a human from computer programs
autonomously. In this way, owners can protect their web services from bots. In
addition to visual CAPTCHAs which consist of distorted images, mostly test
images, that a user must write some description about that image, there are a
significant amount of audio CAPTCHAs as well. Briefly, audio CAPTCHAs are sound
files which consist of human sound under heavy noise where the speaker
pronounces a bunch of digits consecutively. Generally, in those sound files,
there are some periodic and non-periodic noises to get difficult to recognize
them with a program but not for a human listener. We gathered numerous randomly
collected audio file to train and then test them using our SVM algorithm to be
able to extract digits out of each conversation.
Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps
Authors: Abhishek Sehgal, Nasser Kehtarnavaz
Date: 2019-01-08 00:00:00
Tasks: Benchmarking, CPU, Deep Learning, Edge-computing, GPU, Image Classification

Deep learning solutions are being increasingly used in mobile applications.
Although there are many open-source software tools for the development of deep
learning solutions, there are no guidelines in one place in a unified manner
for using these tools towards real-time deployment of these solutions on
smartphones. From the variety of available deep learning tools, the most suited
ones are used in this paper to enable real-time deployment of deep learning
inference networks on smartphones. A uniform flow of implementation is devised
for both Android and iOS smartphones. The advantage of using multi-threading to
achieve or improve real-time throughputs is also showcased. A benchmarking
framework consisting of accuracy, CPU/GPU consumption and real-time throughput
is considered for validation purposes. The developed deployment approach allows
deep learning models to be turned into real-time smartphone apps with ease
based on publicly available deep learning and smartphone software tools. This
approach is applied to six popular or representative convolutional neural
network models and the validation results based on the benchmarking metrics are
reported.
Spatial-Winograd Pruning Enabling Sparse Winograd Convolution
Authors: Jiecao Yu, Jongsoo Park, Maxim Naumov
Date: 2019-01-08 00:00:00
Proceeding: ICLR 2019 5

Deep convolutional neural networks (CNNs) are deployed in various
applications but demand immense computational requirements. Pruning techniques
and Winograd convolution are two typical methods to reduce the CNN computation.
However, they cannot be directly combined because Winograd transformation fills
in the sparsity resulting from pruning. Li et al. (2017) propose sparse
Winograd convolution in which weights are directly pruned in the Winograd
domain, but this technique is not very practical because Winograd-domain
retraining requires low learning rates and hence significantly longer training
time. Besides, Liu et al. (2018) move the ReLU function into the Winograd
domain, which can help increase the weight sparsity but requires changes in the
network structure. To achieve a high Winograd-domain weight sparsity without
changing network structures, we propose a new pruning method, spatial-Winograd
pruning. As the first step, spatial-domain weights are pruned in a structured
way, which efficiently transfers the spatial-domain sparsity into the Winograd
domain and avoids Winograd-domain retraining. For the next step, we also
perform pruning and retraining directly in the Winograd domain but propose to
use an importance factor matrix to adjust weight importance and weight
gradients. This adjustment makes it possible to effectively retrain the pruned
Winograd-domain network without changing the network structure. For the three
models on the datasets of CIFAR10, CIFAR-100, and ImageNet, our proposed method
can achieve the Winograd domain sparsities of 63%, 50%, and 74%, respectively.
Dynamics are Important for the Recognition of Equine Pain in Video
Authors: Sofia Broomé, Karina Bech Gleerup, Pia Haubro Andersen, Hedvig Kjellström
Date: 2019-01-07 00:00:00

A prerequisite to successfully alleviate pain in animals is to recognize it, which is a great challenge in non-verbal species. Furthermore, prey animals such as horses tend to hide their pain. In this study, we propose a deep recurrent two-stream architecture for the task of distinguishing pain from non-pain in videos of horses. Different models are evaluated on a unique dataset showing horses under controlled trials with moderate pain induction, which has been presented in earlier work. Sequential models are experimentally compared to single-frame models, showing the importance of the temporal dimension of the data, and are benchmarked against a veterinary expert classification of the data. We additionally perform baseline comparisons with generalized versions of state-of-the-art human pain recognition methods. While equine pain detection in machine learning is a novel field, our results surpass veterinary expert performance and outperform pain detection results reported for other larger non-human species.
On the effect of the activation function on the distribution of hidden nodes in a deep network
Authors: Philip M. Long, Hanie Sedghi
Date: 2019-01-07 00:00:00

We analyze the joint probability distribution on the lengths of the vectors
of hidden variables in different layers of a fully connected deep network, when
the weights and biases are chosen randomly according to Gaussian distributions,
and the input is in $\{ -1, 1\}^N$. We show that, if the activation function
$\phi$ satisfies a minimal set of assumptions, satisfied by all activation
functions that we know that are used in practice, then, as the width of the
network gets large, the `length process' converges in probability to a length
map that is determined as a simple function of the variances of the random
weights and biases, and the activation function $\phi$. We also show that this
convergence may fail for $\phi$ that violate our assumptions.
On the Dimensionality of Embeddings for Sparse Features and Data
Authors: Maxim Naumov
Date: 2019-01-07 00:00:00

In this note we discuss a common misconception, namely that embeddings are
always used to reduce the dimensionality of the item space. We show that when
we measure dimensionality in terms of information entropy then the embedding of
sparse probability distributions, that can be used to represent sparse features
or data, may or not reduce the dimensionality of the item space. However, the
embeddings do provide a different and often more meaningful representation of
the items for a particular task at hand. Also, we give upper bounds and more
precise guidelines for choosing the embedding dimension.
Differentially Private ADMM for Distributed Medical Machine Learning
Authors: Jiahao Ding, Xiaoqi Qin, Wenjun Xu, Yanmin Gong, Chi Zhang, Miao Pan
Date: 2019-01-07 00:00:00
Tasks: BIG-bench Machine Learning

Due to massive amounts of data distributed across multiple locations, distributed machine learning has attracted a lot of research interests. Alternating Direction Method of Multipliers (ADMM) is a powerful method of designing distributed machine learning algorithm, whereby each agent computes over local datasets and exchanges computation results with its neighbor agents in an iterative procedure. There exists significant privacy leakage during this iterative process if the local data is sensitive. In this paper, we propose a differentially private ADMM algorithm (P-ADMM) to provide dynamic zero-concentrated differential privacy (dynamic zCDP), by inserting Gaussian noise with linearly decaying variance. We prove that P-ADMM has the same convergence rate compared to the non-private counterpart, i.e., $\mathcal{O}(1/K)$ with $K$ being the number of iterations and linear convergence for general convex and strongly convex problems while providing differentially private guarantee. Moreover, through our experiments performed on real-world datasets, we empirically show that P-ADMM has the best-known performance among the existing differentially private ADMM based algorithms.
Team EP at TAC 2018: Automating data extraction in systematic reviews of environmental agents
Authors: Artur Nowak, Paweł Kunstman
Date: 2019-01-07 00:00:00
Tasks: Word Embeddings

We describe our entry for the Systematic Review Information Extraction track
of the 2018 Text Analysis Conference. Our solution is an end-to-end, deep
learning, sequence tagging model based on the BI-LSTM-CRF architecture.
However, we use interleaved, alternating LSTM layers with highway connections
instead of the more traditional approach, where last hidden states of both
directions are concatenated to create an input to the next layer. We also make
extensive use of pre-trained word embeddings, namely GloVe and ELMo. Thanks to
a number of regularization techniques, we were able to achieve relatively large
capacity of the model (31.3M+ of trainable parameters) for the size of training
set (100 documents, less than 200K tokens). The system's official score was
60.9% (micro-F1) and it ranked first for the Task 1. Additionally, after
rectifying an obvious mistake in the submission format, the system scored
67.35%.
All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks
Authors: Stephen Phillips, Kostas Daniilidis
Date: 2019-01-07 00:00:00
Tasks: All, Graph Embedding

Image feature matching is a fundamental part of many geometric computer
vision applications, and using multiple images can improve performance. In this
work, we formulate multi-image matching as a graph embedding problem then use a
Graph Convolutional Network to learn an appropriate embedding function for
aligning image features. We use cycle consistency to train our network in an
unsupervised fashion, since ground truth correspondence is difficult or
expensive to aquire. In addition, geometric consistency losses can be added at
training time, even if the information is not available in the test set, unlike
previous approaches that optimize cycle consistency directly. To the best of
our knowledge, no other works have used learning for multi-image feature
matching. Our experiments show that our method is competitive with other
optimization based approaches.
Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation
Authors: Chiyu "Max" Jiang, Dequan Wang, Jingwei Huang, Philip Marcus, Matthias Nießner
Date: 2019-01-07 00:00:00
Proceeding: ICLR 2019 5
Tasks: 3D Shape Classification, 3D Shape Retrieval, Retrieval, Surface Reconstruction

Convolutional Neural Networks (CNN) have been successful in processing data
signals that are uniformly sampled in the spatial domain (e.g., images).
However, most data signals do not natively exist on a grid, and in the process
of being sampled onto a uniform physical grid suffer significant aliasing error
and information loss. Moreover, signals can exist in different topological
structures as, for example, points, lines, surfaces and volumes. It has been
challenging to analyze signals with mixed topologies (for example, point cloud
with surface mesh). To this end, we develop mathematical formulations for
Non-Uniform Fourier Transforms (NUFT) to directly, and optimally, sample
nonuniform data signals of different topologies defined on a simplex mesh into
the spectral domain with no spatial sampling error. The spectral transform is
performed in the Euclidean space, which removes the translation ambiguity from
works on the graph spectrum. Our representation has four distinct advantages:
(1) the process causes no spatial sampling error during the initial sampling,
(2) the generality of this approach provides a unified framework for using CNNs
to analyze signals of mixed topologies, (3) it allows us to leverage
state-of-the-art backbone CNN architectures for effective learning without
having to design a particular architecture for a particular data structure in
an ad-hoc fashion, and (4) the representation allows weighted meshes where each
element has a different weight (i.e., texture) indicating local properties. We
achieve results on par with the state-of-the-art for the 3D shape retrieval
task, and a new state-of-the-art for the point cloud to surface reconstruction
task.