pwc-archive/papers-with-abstracts huggingface dataset:
Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN
Authors: Shiyi Lan, Ruichi Yu, Gang Yu, Larry S. Davis
Date: 2018-11-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: Modeling Local Geometric Structure

Recent advances in deep convolutional neural networks (CNNs) have motivated
researchers to adapt CNNs to directly model points in 3D point clouds. Modeling
local structure has been proven to be important for the success of
convolutional architectures, and researchers exploited the modeling of local
point sets in the feature extraction hierarchy. However, limited attention has
been paid to explicitly model the geometric structure amongst points in a local
region. To address this problem, we propose Geo-CNN, which applies a generic
convolution-like operation dubbed as GeoConv to each point and its local
neighborhood. Local geometric relationships among points are captured when
extracting edge features between the center and its neighboring points. We
first decompose the edge feature extraction process onto three orthogonal
bases, and then aggregate the extracted features based on the angles between
the edge vector and the bases. This encourages the network to preserve the
geometric structure in Euclidean space throughout the feature extraction
hierarchy. GeoConv is a generic and efficient operation that can be easily
integrated into 3D point cloud analysis pipelines for multiple applications. We
evaluate Geo-CNN on ModelNet40 and KITTI and achieve state-of-the-art
performance.
Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks
Authors: Anton S. Becker, Lukas Jendele, Ondrej Skopek, Nicole Berger, Soleen Ghafoor, Magda Marcon, Ender Konukoglu
Date: 2018-11-19 00:00:00
Tasks: Adversarial Attack, Generative Adversarial Network

$\textbf{Purpose}$ To train a cycle-consistent generative adversarial network
(CycleGAN) on mammographic data to inject or remove features of malignancy, and
to determine whether these AI-mediated attacks can be detected by radiologists.
$\textbf{Material and Methods}$ From the two publicly available datasets, BCDR
and INbreast, we selected images from cancer patients and healthy controls. An
internal dataset served as test data, withheld during training. We ran two
experiments training CycleGAN on low and higher resolution images ($256 \times
256$ px and $512 \times 408$ px). Three radiologists read the images and rated
the likelihood of malignancy on a scale from 1-5 and the likelihood of the
image being manipulated. The readout was evaluated by ROC analysis (Area under
the ROC curve = AUC). $\textbf{Results}$ At the lower resolution, only one
radiologist exhibited markedly lower detection of cancer (AUC=0.85 vs 0.63,
p=0.06), while the other two were unaffected (0.67 vs. 0.69 and 0.75 vs. 0.77,
p=0.55). Only one radiologist could discriminate between original and modified
images slightly better than guessing/chance (0.66, p=0.008). At the higher
resolution, all radiologists showed significantly lower detection rate of
cancer in the modified images (0.77-0.84 vs. 0.59-0.69, p=0.008), however, they
were now able to reliably detect modified images due to better visibility of
artifacts (0.92, 0.92 and 0.97). $\textbf{Conclusion}$ A CycleGAN can
implicitly learn malignant features and inject or remove them so that a
substantial proportion of small mammographic images would consequently be
misdiagnosed. At higher resolutions, however, the method is currently limited
and has a clear trade-off between manipulation of images and introduction of
artifacts.
How to Use Heuristics for Differential Privacy
Authors: Seth Neel, Aaron Roth, Zhiwei Steven Wu
Date: 2018-11-19 00:00:00
Tasks: PAC learning

We develop theory for using heuristics to solve computationally hard problems
in differential privacy. Heuristic approaches have enjoyed tremendous success
in machine learning, for which performance can be empirically evaluated.
However, privacy guarantees cannot be evaluated empirically, and must be proven
--- without making heuristic assumptions. We show that learning problems over
broad classes of functions can be solved privately and efficiently, assuming
the existence of a non-private oracle for solving the same problem. Our first
algorithm yields a privacy guarantee that is contingent on the correctness of
the oracle. We then give a reduction which applies to a class of heuristics
which we call certifiable, which allows us to convert oracle-dependent privacy
guarantees to worst-case privacy guarantee that hold even when the heuristic
standing in for the oracle might fail in adversarial ways. Finally, we consider
a broad class of functions that includes most classes of simple boolean
functions studied in the PAC learning literature, including conjunctions,
disjunctions, parities, and discrete halfspaces. We show that there is an
efficient algorithm for privately constructing synthetic data for any such
class, given a non-private learning oracle. This in particular gives the first
oracle-efficient algorithm for privately generating synthetic data for
contingency tables. The most intriguing question left open by our work is
whether or not every problem that can be solved differentially privately can be
privately solved with an oracle-efficient algorithm. While we do not resolve
this, we give a barrier result that suggests that any generic oracle-efficient
reduction must fall outside of a natural class of algorithms (which includes
the algorithms given in this paper).
Decentralized Exploration in Multi-Armed Bandits -- Extended version
Authors: Raphaël Féraud, Réda Alami, Romain Laroche
Date: 2018-11-19 00:00:00
Tasks: Multi-Armed Bandits

We consider the decentralized exploration problem: a set of players collaborate to identify the best arm by asynchronously interacting with the same stochastic environment. The objective is to insure privacy in the best arm identification problem between asynchronous, collaborative, and thrifty players. In the context of a digital service, we advocate that this decentralized approach allows a good balance between the interests of users and those of service providers: the providers optimize their services, while protecting the privacy of the users and saving resources. We define the privacy level as the amount of information an adversary could infer by intercepting the messages concerning a single user. We provide a generic algorithm Decentralized Elimination, which uses any best arm identification algorithm as a subroutine. We prove that this algorithm insures privacy, with a low communication cost, and that in comparison to the lower bound of the best arm identification problem, its sample complexity suffers from a penalty depending on the inverse of the probability of the most frequent players. Then, thanks to the genericity of the approach, we extend the proposed algorithm to the non-stationary bandits. Finally, experiments illustrate and complete the analysis.
Building Efficient Deep Neural Networks with Unitary Group Convolutions
Authors: Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang
Date: 2018-11-19 00:00:00
Proceeding: CVPR 2019 6

We propose unitary group convolutions (UGConvs), a building block for CNNs
which compose a group convolution with unitary transforms in feature space to
learn a richer set of representations than group convolution alone. UGConvs
generalize two disparate ideas in CNN architecture, channel shuffling (i.e.
ShuffleNet) and block-circulant networks (i.e. CirCNN), and provide unifying
insights that lead to a deeper understanding of each technique. We
experimentally demonstrate that dense unitary transforms can outperform channel
shuffling in DNN accuracy. On the other hand, different dense transforms
exhibit comparable accuracy performance. Based on these observations we propose
HadaNet, a UGConv network using Hadamard transforms. HadaNets achieve similar
accuracy to circulant networks with lower computation complexity, and better
accuracy than ShuffleNets with the same number of parameters and floating-point
multiplies.
Contextual Face Recognition with a Nested-Hierarchical Nonparametric Identity Model
Authors: Daniel C. Castro, Sebastian Nowozin
Date: 2018-11-19 00:00:00
Tasks: Face Recognition, General Classification

Current face recognition systems typically operate via classification into
known identities obtained from supervised identity annotations. There are two
problems with this paradigm: (1) current systems are unable to benefit from
often abundant unlabelled data; and (2) they equate successful recognition with
labelling a given input image. Humans, on the other hand, regularly perform
identification of individuals completely unsupervised, recognising the identity
of someone they have seen before even without being able to name that
individual. How can we go beyond the current classification paradigm towards a
more human understanding of identities? In previous work, we proposed an
integrated Bayesian model that coherently reasons about the observed images,
identities, partial knowledge about names, and the situational context of each
observation. Here, we propose extensions of the contextual component of this
model, enabling unsupervised discovery of an unbounded number of contexts for
improved face recognition.
Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data
Authors: Stefan Schneider, Graham W. Taylor, Stefan S. Linquist, Stefan C. Kremer
Date: 2018-11-19 00:00:00
Tasks: Deep Learning, Feature Engineering, object-detection, Object Detection

The ability of a researcher to re-identify (re-ID) an individual animal upon
re-encounter is fundamental for addressing a broad range of questions in the
study of ecosystem function, community and population dynamics, and behavioural
ecology. In this review, we describe a brief history of camera traps for re-ID,
present a collection of computer vision feature engineering methodologies
previously used for animal re-ID, provide an introduction to the underlying
mechanisms of deep learning relevant to animal re-ID, highlight the success of
deep learning methods for human re-ID, describe the few ecological studies
currently utilizing deep learning for camera trap analyses, and our predictions
for near future methodologies based on the rapid development of deep learning
methods. By utilizing novel deep learning methods for object detection and
similarity comparisons, ecologists can extract animals from an image/video data
and train deep learning classifiers to re-ID animal individuals beyond the
capabilities of a human observer. This methodology will allow ecologists with
camera/video trap data to re-identify individuals that exit and re-enter the
camera frame. Our expectation is that this is just the beginning of a major
trend that could stand to revolutionize the analysis of camera trap data and,
ultimately, our approach to animal ecology.
How far from automatically interpreting deep learning
Authors: Jinwei Zhao, Qizhou Wang, YuFei Wang, Xinhong Hei, Yu Liu
Date: 2018-11-19 00:00:00
Tasks: Deep Learning

In recent years, deep learning researchers have focused on how to find the
interpretability behind deep learning models. However, today cognitive
competence of human has not completely covered the deep learning model. In
other words, there is a gap between the deep learning model and the cognitive
mode. How to evaluate and shrink the cognitive gap is a very important issue.
In this paper, the interpretability evaluation, the relationship between the
generalization performance and the interpretability of the model and the method
for improving the interpretability are concerned. A universal learning
framework is put forward to solve the equilibrium problem between the two
performances. The uniqueness of solution of the problem is proved and condition
of unique solution is obtained. Probability upper bound of the sum of the two
performances is analyzed.
Reinforcement Learning with A* and a Deep Heuristic
Authors: Ariel Keselman, Sergey Ten, Adham Ghazali, Majed Jubeh
Date: 2018-11-19 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A* is a popular path-finding algorithm, but it can only be applied to those
domains where a good heuristic function is known. Inspired by recent methods
combining Deep Neural Networks (DNNs) and trees, this study demonstrates how to
train a heuristic represented by a DNN and combine it with A*. This new
algorithm which we call aleph-star can be used efficiently in domains where the
input to the heuristic could be processed by a neural network. We compare
aleph-star to N-Step Deep Q-Learning (DQN Mnih et al. 2013) in a driving
simulation with pixel-based input, and demonstrate significantly better
performance in this scenario.
M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments
Authors: Tim Laibacher, Tillman Weyde, Sepehr Jalali
Date: 2018-11-19 00:00:00
Tasks: Decoder, GPU, Retinal Vessel Segmentation

In this paper, we present a novel neural network architecture for retinal
vessel segmentation that improves over the state of the art on two benchmark
datasets, is the first to run in real time on high resolution images, and its
small memory and processing requirements make it deployable in mobile and
embedded systems. The M2U-Net has a new encoder-decoder architecture that is
inspired by the U-Net. It adds pretrained components of MobileNetV2 in the
encoder part and novel contractive bottleneck blocks in the decoder part that,
combined with bilinear upsampling, drastically reduce the parameter count to
0.55M compared to 31.03M in the original U-Net. We have evaluated its
performance against a wide body of previously published results on three public
datasets. On two of them, the M2U-Net achieves new state-of-the-art performance
by a considerable margin. When implemented on a GPU, our method is the first to
achieve real-time inference speeds on high-resolution fundus images. We also
implemented our proposed network on an ARM-based embedded system where it
segments images in between 0.6 and 15 sec, depending on the resolution. Thus,
the M2U-Net enables a number of applications of retinal vessel structure
extraction, such as early diagnosis of eye diseases, retinal biometric
authentication systems, and robot assisted microsurgery.
Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?
Authors: Ping Luo, Zhanglin Peng, Jiamin Ren, Ruimao Zhang
Date: 2018-11-19 00:00:00

Yes, they do. This work investigates a perspective for deep learning: whether
different normalization layers in a ConvNet require different normalizers. This
is the first step towards understanding this phenomenon. We allow each
convolutional layer to be stacked before a switchable normalization (SN) that
learns to choose a normalizer from a pool of normalization methods. Through
systematic experiments in ImageNet, COCO, Cityscapes, and ADE20K, we answer
three questions: (a) Is it useful to allow each normalization layer to select
its own normalizer? (b) What impacts the choices of normalizers? (c) Do
different tasks and datasets prefer different normalizers? Our results suggest
that (1) using distinct normalizers improves both learning and generalization
of a ConvNet; (2) the choices of normalizers are more related to depth and
batch size, but less relevant to parameter initialization, learning rate decay,
and solver; (3) different tasks and datasets have different behaviors when
learning to select normalizers.
Towards Global Explanations for Credit Risk Scoring
Authors: Irene Unceta, Jordi Nin, Oriol Pujol
Date: 2018-11-19 00:00:00
Tasks: General Classification

In this paper we propose a method to obtain global explanations for trained
black-box classifiers by sampling their decision function to learn alternative
interpretable models. The envisaged approach provides a unified solution to
approximate non-linear decision boundaries with simpler classifiers while
retaining the original classification accuracy. We use a private residential
mortgage default dataset as a use case to illustrate the feasibility of this
approach to ensure the decomposability of attributes during pre-processing.
Efficient keyword spotting using dilated convolutions and gating
Authors: Alice Coucke, Mohammed Chlieh, Thibault Gisselbrecht, David Leroy, Mathieu Poumeyrol, Thibaut Lavril
Date: 2018-11-19 00:00:00
Tasks: Keyword Spotting, Small-Footprint Keyword Spotting

We explore the application of end-to-end stateless temporal modeling to
small-footprint keyword spotting as opposed to recurrent networks that model
long-term temporal dependencies using internal states. We propose a model
inspired by the recent success of dilated convolutions in sequence modeling
applications, allowing to train deeper architectures in resource-constrained
configurations. Gated activations and residual connections are also added,
following a similar configuration to WaveNet. In addition, we apply a custom
target labeling that back-propagates loss from specific frames of interest,
therefore yielding higher accuracy and only requiring to detect the end of the
keyword. Our experimental results show that our model outperforms a max-pooling
loss trained recurrent neural network using LSTM cells, with a significant
decrease in false rejection rate. The underlying dataset - "Hey Snips"
utterances recorded by over 2.2K different speakers - has been made publicly
available to establish an open reference for wake-word detection.
An Adaptive Oversampling Learning Method for Class-Imbalanced Fault Diagnostics and Prognostics
Authors: Wenfang Lin, Zhen-Yu Wu, Yang Ji
Date: 2018-11-19 00:00:00
Tasks: Imputation

Data-driven fault diagnostics and prognostics suffers from class-imbalance
problem in industrial systems and it raises challenges to common machine
learning algorithms as it becomes difficult to learn the features of the
minority class samples. Synthetic oversampling methods are commonly used to
tackle these problems by generating the minority class samples to balance the
distributions between majority and minority classes. However, many of
oversampling methods are inappropriate that they cannot generate effective and
useful minority class samples according to different distributions of data,
which further complicate the process of learning samples. Thus, this paper
proposes a novel adaptive oversampling technique: EM-based Weighted Minority
Oversampling TEchnique (EWMOTE) for industrial fault diagnostics and
prognostics. The methods comprises a weighted minority sampling strategy to
identify hard-to-learn informative minority fault samples and Expectation
Maximization (EM) based imputation algorithm to generate fault samples. To
validate the performance of the proposed methods, experiments are conducted in
two real datasets. The results show that the method could achieve better
performance on not only binary class, but multi-class imbalance learning task
in different imbalance ratios than other oversampling-based baseline models.
When Conventional machine learning meets neuromorphic engineering: Deep Temporal Networks (DTNets) a machine learning frawmework allowing to operate on Events and Frames and implantable on Tensor Flow Like Hardware
Authors: Marco Macanovic, Fabian Chersi, Felix Rutard, Sio-Hoi Ieng, Ryad Benosman
Date: 2018-11-19 00:00:00
Tasks: BIG-bench Machine Learning

We introduce in this paper the principle of Deep Temporal Networks that allow
to add time to convolutional networks by allowing deep integration principles
not only using spatial information but also increasingly large temporal window.
The concept can be used for conventional image inputs but also event based
data. Although inspired by the architecture of brain that inegrates information
over increasingly larger spatial but also temporal scales it can operate on
conventional hardware using existing architectures. We introduce preliminary
results to show the efficiency of the method. More in-depth results and
analysis will be reported soon!
FD-GAN: Face-demorphing generative adversarial network for restoring accomplice's facial image
Authors: Fei Peng, Le-bing Zhang, Min Long
Date: 2018-11-19 00:00:00
Tasks: Face Recognition, Generative Adversarial Network

Face morphing attack is proved to be a serious threat to the existing face
recognition systems. Although a few face morphing detection methods have been
put forward, the face morphing accomplice's facial restoration remains a
challenging problem. In this paper, a face de-morphing generative adversarial
network (FD-GAN) is proposed to restore the accomplice's facial image. It
utilizes a symmetric dual network architecture and two levels of restoration
losses to separate the identity feature of the morphing accomplice. By
exploiting the captured facial image (containing the criminal's identity) from
the face recognition system and the morphed image stored in the e-passport
system (containing both criminal and accomplice's identities), the FD-GAN can
effectively restore the accomplice's facial image. Experimental results and
analysis demonstrate the effectiveness of the proposed scheme. It has great
potential to be implemented for detecting the face morphing accomplice in a
real identity verification scenario.
Intention Oriented Image Captions with Guiding Objects
Authors: Yue Zheng, Ya-Li Li, Shengjin Wang
Date: 2018-11-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Captioning, Object

Although existing image caption models can produce promising results using
recurrent neural networks (RNNs), it is difficult to guarantee that an object
we care about is contained in generated descriptions, for example in the case
that the object is inconspicuous in the image. Problems become even harder when
these objects did not appear in training stage. In this paper, we propose a
novel approach for generating image captions with guiding objects (CGO). The
CGO constrains the model to involve a human-concerned object when the object is
in the image. CGO ensures that the object is in the generated description while
maintaining fluency. Instead of generating the sequence from left to right, we
start the description with a selected object and generate other parts of the
sequence based on this object. To achieve this, we design a novel framework
combining two LSTMs in opposite directions. We demonstrate the characteristics
of our method on MSCOCO where we generate descriptions for each detected object
in the images. With CGO, we can extend the ability of description to the
objects being neglected in image caption labels and provide a set of more
comprehensive and diverse descriptions for an image. CGO shows advantages when
applied to the task of describing novel objects. We show experimental results
on both MSCOCO and ImageNet datasets. Evaluations show that our method
outperforms the state-of-the-art models in the task with average F1 75.8,
leading to better descriptions in terms of both content accuracy and fluency.
An Influence-based Clustering Model on Twitter
Authors: Abbas Ehsanfar, Mo Mansouri
Date: 2018-11-19 00:00:00
Tasks: Clustering, model

This paper introduces a temporal framework for detecting and clustering
emergent and viral topics on social networks. Endogenous and exogenous
influence on developing viral content is explored using a clustering method
based on the a user's behavior on social network and a dataset from Twitter
API. Results are discussed by introducing metrics such as popularity,
burstiness, and relevance score. The results show clear distinction in
characteristics of developed content by the two classes of users.