pwc-archive/papers-with-abstracts huggingface dataset:
Regularized adversarial examples for model interpretability
Authors: Yoel Shoshan, Vadim Ratner
Date: 2018-11-18 00:00:00
Tasks: model

As machine learning algorithms continue to improve, there is an increasing
need for explaining why a model produces a certain prediction for a certain
input. In recent years, several methods for model interpretability have been
developed, aiming to provide explanation of which subset regions of the model
input is the main reason for the model prediction. In parallel, a significant
research community effort is occurring in recent years for developing
adversarial example generation methods for fooling models, while not altering
the true label of the input,as it would have been classified by a human
annotator. In this paper, we bridge the gap between adversarial example
generation and model interpretability, and introduce a modification to the
adversarial example generation process which encourages better
interpretability. We analyze the proposed method on a public medical imaging
dataset, both quantitatively and qualitatively, and show that it significantly
outperforms the leading known alternative method. Our suggested method is
simple to implement, and can be easily plugged into most common adversarial
example generation frameworks. Additionally, we propose an explanation quality
metric - $APE$ - "Adversarial Perturbative Explanation", which measures how
well an explanation describes model decisions.
A Variational Dirichlet Framework for Out-of-Distribution Detection
Authors: Wenhu Chen, Yilin Shen, Hongxia Jin, William Wang
Date: 2018-11-18 00:00:00
Proceeding: ICLR 2019 5
Tasks: Out-of-Distribution Detection, Variational Inference

With the recently rapid development in deep learning, deep neural networks
have been widely adopted in many real-life applications. However, deep neural
networks are also known to have very little control over its uncertainty for
unseen examples, which potentially causes very harmful and annoying
consequences in practical scenarios. In this paper, we are particularly
interested in designing a higher-order uncertainty metric for deep neural
networks and investigate its effectiveness under the out-of-distribution
detection task proposed by~\cite{hendrycks2016baseline}. Our method first
assumes there exists an underlying higher-order distribution $\mathbb{P}(z)$,
which controls label-wise categorical distribution $\mathbb{P}(y)$ over classes
on the K-dimension simplex, and then approximate such higher-order distribution
via parameterized posterior function $p_{\theta}(z|x)$ under variational
inference framework, finally we use the entropy of learned posterior
distribution $p_{\theta}(z|x)$ as uncertainty measure to detect
out-of-distribution examples. Further, we propose an auxiliary objective
function to discriminate against synthesized adversarial examples to further
increase the robustness of the proposed uncertainty measure. Through
comprehensive experiments on various datasets, our proposed framework is
demonstrated to consistently outperform competing algorithms.
GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint
Authors: Jianlin Su
Date: 2018-11-18 00:00:00
Tasks: Articles, Generative Adversarial Network

We know SGAN may have a risk of gradient vanishing. A significant improvement
is WGAN, with the help of 1-Lipschitz constraint on discriminator to prevent
from gradient vanishing. Is there any GAN having no gradient vanishing and no
1-Lipschitz constraint on discriminator? We do find one, called GAN-QP.
  To construct a new framework of Generative Adversarial Network (GAN) usually
includes three steps: 1. choose a probability divergence; 2. convert it into a
dual form; 3. play a min-max game. In this articles, we demonstrate that the
first step is not necessary. We can analyse the property of divergence and even
construct new divergence in dual space directly. As a reward, we obtain a
simpler alternative of WGAN: GAN-QP. We demonstrate that GAN-QP have a better
performance than WGAN in theory and practice.
Image-to-GPS Verification Through A Bottom-Up Pattern Matching Network
Authors: Jiaxin Cheng, Yue Wu, Wael Abd-Almageed, Prem Natarajan
Date: 2018-11-18 00:00:00
Tasks: Image-To-Gps Verification

The image-to-GPS verification problem asks whether a given image is taken at
a claimed GPS location. In this paper, we treat it as an image verification
problem -- whether a query image is taken at the same place as a reference
image retrieved at the claimed GPS location. We make three major contributions:
1) we propose a novel custom bottom-up pattern matching (BUPM) deep neural
network solution; 2) we demonstrate that the verification can be directly done
by cross-checking a perspective-looking query image and a panorama reference
image, and 3) we collect and clean a dataset of 30K pairs query and reference.
Our experimental results show that the proposed BUPM solution outperforms the
state-of-the-art solutions in terms of both verification and localization.
Understanding Learned Models by Identifying Important Features at the Right Resolution
Authors: Kyubin Lee, Akshay Sood, Mark Craven
Date: 2018-11-18 00:00:00
Tasks: Two-sample testing

In many application domains, it is important to characterize how complex
learned models make their decisions across the distribution of instances. One
way to do this is to identify the features and interactions among them that
contribute to a model's predictive accuracy. We present a model-agnostic
approach to this task that makes the following specific contributions. Our
approach (i) tests feature groups, in addition to base features, and tries to
determine the level of resolution at which important features can be
determined, (ii) uses hypothesis testing to rigorously assess the effect of
each feature on the model's loss, (iii) employs a hierarchical approach to
control the false discovery rate when testing feature groups and individual
base features for importance, and (iv) uses hypothesis testing to identify
important interactions among features and feature groups. We evaluate our
approach by analyzing random forest and LSTM neural network models learned in
two challenging biomedical applications.
RePr: Improved Training of Convolutional Filters
Authors: Aaditya Prakash, James Storer, Dinei Florencio, Cha Zhang
Date: 2018-11-18 00:00:00
Proceeding: CVPR 2019 6

A well-trained Convolutional Neural Network can easily be pruned without
significant loss of performance. This is because of unnecessary overlap in the
features captured by the network's filters. Innovations in network architecture
such as skip/dense connections and Inception units have mitigated this problem
to some extent, but these improvements come with increased computation and
memory requirements at run-time. We attempt to address this problem from
another angle - not by changing the network structure but by altering the
training method. We show that by temporarily pruning and then restoring a
subset of the model's filters, and repeating this process cyclically, overlap
in the learned features is reduced, producing improved generalization. We show
that the existing model-pruning criteria are not optimal for selecting filters
to prune in this context and introduce inter-filter orthogonality as the
ranking criteria to determine under-expressive filters. Our method is
applicable both to vanilla convolutional networks and more complex modern
architectures, and improves the performance across a variety of tasks,
especially when applied to smaller networks.
CIFAR10 to Compare Visual Recognition Performance between Deep Neural Networks and Humans
Authors: Tien Ho-Phuoc
Date: 2018-11-18 00:00:00
Tasks: General Classification, Object, Object Recognition

Visual object recognition plays an essential role in human daily life. This ability is so efficient that we can recognize a face or an object seemingly without effort, though they may vary in position, scale, pose, and illumination. In the field of computer vision, a large number of studies have been carried out to build a human-like object recognition system. Recently, deep neural networks have shown impressive progress in object classification performance, and have been reported to surpass humans. Yet there is still lack of thorough and fair comparison between humans and artificial recognition systems. While some studies consider artificially degraded images, human recognition performance on dataset widely used for deep neural networks has not been fully evaluated. The present paper carries out an extensive experiment to evaluate human classification accuracy on CIFAR10, a well-known dataset of natural images. This then allows for a fair comparison with the state-of-the-art deep neural networks. Our CIFAR10-based evaluations show very efficient object recognition of recent CNNs but, at the same time, prove that they are still far from human-level capability of generalization. Moreover, a detailed investigation using multiple levels of difficulty reveals that easy images for humans may not be easy for deep neural networks. Such images form a subset of CIFAR10 that can be employed to evaluate and improve future neural networks.
Deep Learning with Inaccurate Training Data for Image Restoration
Authors: Bolin Liu, Xiao Shu, Xiaolin Wu
Date: 2018-11-18 00:00:00
Tasks: Deep Learning, Image Restoration

In many applications of deep learning, particularly those in image
restoration, it is either very difficult, prohibitively expensive, or outright
impossible to obtain paired training data precisely as in the real world. In
such cases, one is forced to use synthesized paired data to train the deep
convolutional neural network (DCNN). However, due to the unavoidable
generalization error in statistical learning, the synthetically trained DCNN
often performs poorly on real world data. To overcome this problem, we propose
a new general training method that can compensate for, to a large extent, the
generalization errors of synthetically trained DCNNs.
Probabilistic Graphs for Sensor Data-driven Modelling of Power Systems at Scale
Authors: Francesco Fusco
Date: 2018-11-18 00:00:00
Tasks: Anomaly Detection, State Estimation

The growing complexity of the power grid, driven by increasing share of
distributed energy resources and by massive deployment of intelligent
internet-connected devices, requires new modelling tools for planning and
operation. Physics-based state estimation models currently used for data
filtering, prediction and anomaly detection are hard to maintain and adapt to
the ever-changing complex dynamics of the power system. A data-driven approach
based on probabilistic graphs is proposed, where custom non-linear, localised
models of the joint density of subset of system variables can be combined to
model arbitrarily large and complex systems. The graphical model allows to
naturally embed domain knowledge in the form of variables dependency structure
or local quantitative relationships. A specific instance where neural-network
models are used to represent the local joint densities is proposed, although
the methodology generalises to other model classes. Accuracy and scalability
are evaluated on a large-scale data set representative of the European
transmission grid.
DeepConsensus: using the consensus of features from multiple layers to attain robust image classification
Authors: Yuchen Li, Safwan Hossain, Kiarash Jamali, Frank Rudzicz
Date: 2018-11-18 00:00:00
Tasks: General Classification, image-classification, Image Classification

We consider a classifier whose test set is exposed to various perturbations
that are not present in the training set. These test samples still contain
enough features to map them to the same class as their unperturbed counterpart.
Current architectures exhibit rapid degradation of accuracy when trained on
standard datasets but then used to classify perturbed samples of that data. To
address this, we present a novel architecture named DeepConsensus that
significantly improves generalization to these test-time perturbations. Our key
insight is that deep neural networks should directly consider summaries of low
and high level features when making classifications. Existing convolutional
neural networks can be augmented with DeepConsensus, leading to improved
resistance against large and small perturbations on MNIST, EMNIST,
FashionMNIST, CIFAR10 and SVHN datasets.
GLStyleNet: Higher Quality Style Transfer Combining Global and Local Pyramid Features
Authors: Zhizhong Wang, Lei Zhao, Wei Xing, Dongming Lu
Date: 2018-11-18 00:00:00
Tasks: Style Transfer

Recent studies using deep neural networks have shown remarkable success in
style transfer especially for artistic and photo-realistic images. However, the
approaches using global feature correlations fail to capture small, intricate
textures and maintain correct texture scales of the artworks, and the
approaches based on local patches are defective on global effect. In this
paper, we present a novel feature pyramid fusion neural network, dubbed
GLStyleNet, which sufficiently takes into consideration multi-scale and
multi-level pyramid features by best aggregating layers across a VGG network,
and performs style transfer hierarchically with multiple losses of different
scales. Our proposed method retains high-frequency pixel information and low
frequency construct information of images from two aspects: loss function
constraint and feature fusion. Our approach is not only flexible to adjust the
trade-off between content and style, but also controllable between global and
local. Compared to state-of-the-art methods, our method can transfer not just
large-scale, obvious style cues but also subtle, exquisite ones, and
dramatically improves the quality of style transfer. We demonstrate the
effectiveness of our approach on portrait style transfer, artistic style
transfer, photo-realistic style transfer and Chinese ancient painting style
transfer tasks. Experimental results indicate that our unified approach
improves image style transfer quality over previous state-of-the-art methods,
while also accelerating the whole process in a certain extent. Our code is
available at https://github.com/EndyWon/GLStyleNet.
Exploit the Connectivity: Multi-Object Tracking with TrackletNet
Authors: Gaoang Wang, Yizhou Wang, Haotian Zhang, Renshu Gu, Jenq-Neng Hwang
Date: 2018-11-18 00:00:00
Tasks: Autonomous Driving, Multi-Object Tracking, Object, Object Tracking

Multi-object tracking (MOT) is an important and practical task related to
both surveillance systems and moving camera applications, such as autonomous
driving and robotic vision. However, due to unreliable detection, occlusion and
fast camera motion, tracked targets can be easily lost, which makes MOT very
challenging. Most recent works treat tracking as a re-identification (Re-ID)
task, but how to combine appearance and temporal features is still not well
addressed. In this paper, we propose an innovative and effective tracking
method called TrackletNet Tracker (TNT) that combines temporal and appearance
information together as a unified framework. First, we define a graph model
which treats each tracklet as a vertex. The tracklets are generated by
appearance similarity with CNN features and intersection-over-union (IOU) with
epipolar constraints to compensate camera movement between adjacent frames.
Then, for every pair of two tracklets, the similarity is measured by our
designed multi-scale TrackletNet. Afterwards, the tracklets are clustered into
groups which represent individual object IDs. Our proposed TNT has the ability
to handle most of the challenges in MOT, and achieve promising results on MOT16
and MOT17 benchmark datasets compared with other state-of-the-art methods.
Optical Flow Based Online Moving Foreground Analysis
Authors: Junjie Huang, Wei Zou, Zheng Zhu, Jiagang Zhu
Date: 2018-11-18 00:00:00
Tasks: Clustering, Moving Object Detection, object-detection, Object Detection, Optical Flow Estimation

Obtained by moving object detection, the foreground mask result is unshaped
and can not be directly used in most subsequent processes. In this paper, we
focus on this problem and address it by constructing an optical flow based
moving foreground analysis framework. During the processing procedure, the
foreground masks are analyzed and segmented through two complementary
clustering algorithms. As a result, we obtain the instance-level information
like the number, location and size of moving objects. The experimental result
show that our method adapts itself to the problem and performs well enough for
practical applications.
Bayesian Modeling of Intersectional Fairness: The Variance of Bias
Authors: James Foulds, Rashidul Islam, Kamrun Keya, SHimei Pan
Date: 2018-11-18 00:00:00
Tasks: Fairness

Intersectionality is a framework that analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability. Intersectionality theory therefore implies it is important that fairness in artificial intelligence systems be protected with regard to multi-dimensional protected attributes. However, the measurement of fairness becomes statistically challenging in the multi-dimensional setting due to data sparsity, which increases rapidly in the number of dimensions, and in the values per dimension. We present a Bayesian probabilistic modeling approach for the reliable, data-efficient estimation of fairness with multi-dimensional protected attributes, which we apply to two existing intersectional fairness metrics. Experimental results on census data and the COMPAS criminal justice recidivism dataset demonstrate the utility of our methodology, and show that Bayesian methods are valuable for the modeling and measurement of fairness in an intersectional context.
Quantifying Uncertainties in Natural Language Processing Tasks
Authors: Yijun Xiao, William Yang Wang
Date: 2018-11-18 00:00:00
Tasks: Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Sentiment Analysis, Uncertainty Quantification

Reliable uncertainty quantification is a first step towards building
explainable, transparent, and accountable artificial intelligent systems.
Recent progress in Bayesian deep learning has made such quantification
realizable. In this paper, we propose novel methods to study the benefits of
characterizing model and data uncertainties for natural language processing
(NLP) tasks. With empirical experiments on sentiment analysis, named entity
recognition, and language modeling using convolutional and recurrent neural
network models, we show that explicitly modeling uncertainties is not only
necessary to measure output confidence levels, but also useful at enhancing
model performances in various NLP tasks.
Iris Presentation Attack Detection Based on Photometric Stereo Features
Authors: Adam Czajka, Zhaoyuan Fang, Kevin W. Bowyer
Date: 2018-11-18 00:00:00
Tasks: Binary Classification, Cross-Domain Iris Presentation Attack Detection

We propose a new iris presentation attack detection method using
three-dimensional features of an observed iris region estimated by photometric
stereo. Our implementation uses a pair of iris images acquired by a common
commercial iris sensor (LG 4000). No hardware modifications of any kind are
required. Our approach should be applicable to any iris sensor that can
illuminate the eye from two different directions. Each iris image in the pair
is captured under near-infrared illumination at a different angle relative to
the eye. Photometric stereo is used to estimate surface normal vectors in the
non-occluded portions of the iris region. The variability of the normal vectors
is used as the presentation attack detection score. This score is larger for a
texture that is irregularly opaque and printed on a convex contact lens, and is
smaller for an authentic iris texture. Thus the problem is formulated as binary
classification into (a) an eye wearing textured contact lens and (b) the
texture of an actual iris surface (possibly seen through a clear contact lens).
Experiments were carried out on a database of approx. 2,900 iris image pairs
acquired from approx. 100 subjects. Our method was able to correctly classify
over 95% of samples when tested on contact lens brands unseen in training, and
over 98% of samples when the contact lens brand was seen during training. The
source codes of the method are made available to other researchers.
Learning Local RGB-to-CAD Correspondences for Object Pose Estimation
Authors: Georgios Georgakis, Srikrishna Karanam, Ziyan Wu, Jana Kosecka
Date: 2018-11-18 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object, Pose Estimation

We consider the problem of 3D object pose estimation. While much recent work has focused on the RGB domain, the reliance on accurately annotated images limits their generalizability and scalability. On the other hand, the easily available CAD models of objects are rich sources of data, providing a large number of synthetically rendered images. In this paper, we solve this key problem of existing methods requiring expensive 3D pose annotations by proposing a new method that matches RGB images to CAD models for object pose estimation. Our key innovations compared to existing work include removing the need for either real-world textures for CAD models or explicit 3D pose annotations for RGB images. We achieve this through a series of objectives that learn how to select keypoints and enforce viewpoint and modality invariance across RGB images and CAD model renderings. We conduct extensive experiments to demonstrate that the proposed method can reliably estimate object pose in RGB images, as well as generalize to object instances not seen during training.
PointConv: Deep Convolutional Networks on 3D Point Clouds
Authors: Wenxuan Wu, Zhongang Qi, Li Fuxin
Date: 2018-11-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, Density Estimation, Semantic Segmentation

Unlike images which are represented in regular dense grids, 3D point clouds are irregular and unordered, hence applying convolution on them can be difficult. In this paper, we extend the dynamic filter to a new convolution operation, named PointConv. PointConv can be applied on point clouds to build deep convolutional networks. We treat convolution kernels as nonlinear functions of the local coordinates of 3D points comprised of weight and density functions. With respect to a given point, the weight functions are learned with multi-layer perceptron networks and density functions through kernel density estimation. The most important contribution of this work is a novel reformulation proposed for efficiently computing the weight functions, which allowed us to dramatically scale up the network and significantly improve its performance. The learned convolution kernel can be used to compute translation-invariant and permutation-invariant convolution on any point set in the 3D space. Besides, PointConv can also be used as deconvolution operators to propagate features from a subsampled point cloud back to its original resolution. Experiments on ModelNet40, ShapeNet, and ScanNet show that deep convolutional neural networks built on PointConv are able to achieve state-of-the-art on challenging semantic segmentation benchmarks on 3D point clouds. Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.