pwc-archive/papers-with-abstracts huggingface dataset:
Machine-assisted annotation of forensic imagery
Authors: Sara Mousavi, Ramin Nabati, Megan Kleeschulte, Audris Mockus
Date: 2019-02-28 00:00:00
Tasks: Segmentation, Transfer Learning

Image collections, if critical aspects of image content are exposed, can spur
research and practical applications in many domains. Supervised machine
learning may be the only feasible way to annotate very large collections, but
leading approaches rely on large samples of completely and accurately annotated
images. In the case of a large forensic collection, we are aiming to annotate,
neither the complete annotation nor the large training samples can be feasibly
produced. We, therefore, investigate ways to assist manual annotation efforts
done by forensic experts. We present a method that can propose both images and
areas within an image likely to contain desired classes. Evaluation of the
method with human annotators showed highly accurate classification that was
strongly helped by transfer learning. The segmentation precision (mAP) was
improved by adding a separate class capturing background, but that did not
affect the recall (mAR). Further work is needed to both increase the accuracy
of segmentation and enhances prediction with additional covariates affecting
decomposition. We hope this effort to be of help in other domains that require
weak segmentation and have limited availability of qualified annotators.
Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings
Authors: Olga Moskvyak, Frederic Maire, Asia O. Armstrong, Feras Dayoub, Mahsa Baktashmotlagh
Date: 2019-02-28 00:00:00
Tasks: Triplet

Visual identification of individual animals that bear unique natural body
markings is an important task in wildlife conservation. The photo databases of
animal markings grow larger and each new observation has to be matched against
thousands of images. Existing photo-identification solutions have constraints
on image quality and appearance of the pattern of interest in the image. These
constraints limit the use of photos from citizen scientists. We present a novel
system for visual re-identification based on unique natural markings that is
robust to occlusions, viewpoint and illumination changes. We adapt methods
developed for face re-identification and implement a deep convolutional neural
network (CNN) to learn embeddings for images of natural markings. The distance
between the learned embedding points provides a dissimilarity measure between
the corresponding input images. The network is optimized using the triplet loss
function and the online semi-hard triplet mining strategy. The proposed
re-identification method is generic and not species specific. We evaluate the
proposed system on image databases of manta ray belly patterns and humpback
whale flukes. To be of practical value and adopted by marine biologists, a
re-identification system needs to have a top-10 accuracy of at least 95%. The
proposed system achieves this performance standard.
Deep Interpretable Non-Rigid Structure from Motion
Authors: Chen Kong, Simon Lucey
Date: 2019-02-28 00:00:00
Tasks: Dictionary Learning

All current non-rigid structure from motion (NRSfM) algorithms are limited
with respect to: (i) the number of images, and (ii) the type of shape
variability they can handle. This has hampered the practical utility of NRSfM
for many applications within vision. In this paper we propose a novel deep
neural network to recover camera poses and 3D points solely from an ensemble of
2D image coordinates. The proposed neural network is mathematically
interpretable as a multi-layer block sparse dictionary learning problem, and
can handle problems of unprecedented scale and shape complexity. Extensive
experiments demonstrate the impressive performance of our approach where we
exhibit superior precision and robustness against all available
state-of-the-art works. The considerable model capacity of our approach affords
remarkable generalization to unseen data. We propose a quality measure (based
on the network weights) which circumvents the need for 3D ground-truth to
ascertain the confidence we have in the reconstruction. Once the network's
weights are estimated (for a non-rigid object) we show how our approach can
effectively recover 3D shape from a single image -- outperforming comparable
methods that rely on direct 3D supervision.
An evolutionary model that satisfies detailed balance
Authors: Jüri Lember, Chris Watkins
Date: 2019-02-27 00:00:00
Tasks: model

We propose a class of evolutionary models that involves an arbitrary exchangeable process as the breeding process and different selection schemes. In those models, a new genome is born according to the breeding process, and then a genome is removed according to the selection scheme that involves fitness. Thus the population size remains constant. The process evolves according to a Markov chain, and, unlike in many other existing models, the stationary distribution -- so called mutation-selection equilibrium -- can be easily found and studied. The behaviour of the stationary distribution when the population size increases is our main object of interest. Several phase-transition theorems are proved.
Learning Logistic Circuits
Authors: Yitao Liang, Guy Van Den Broeck
Date: 2019-02-27 00:00:00
Tasks: General Classification

This paper proposes a new classification model called logistic circuits. On
MNIST and Fashion datasets, our learning algorithm outperforms neural networks
that have an order of magnitude more parameters. Yet, logistic circuits have a
distinct origin in symbolic AI, forming a discriminative counterpart to
probabilistic-logical circuits such as ACs, SPNs, and PSDDs. We show that
parameter learning for logistic circuits is convex optimization, and that a
simple local search algorithm can induce strong model structures from data.
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning
Authors: Zakaria Mhammedi, Wouter M. Koolen, Tim van Erven
Date: 2019-02-27 00:00:00
Tasks: Active Learning, Computational Efficiency

We aim to design adaptive online learning algorithms that take advantage of any special structure that might be present in the learning task at hand, with as little manual tuning by the user as possible. A fundamental obstacle that comes up in the design of such adaptive algorithms is to calibrate a so-called step-size or learning rate hyperparameter depending on variance, gradient norms, etc. A recent technique promises to overcome this difficulty by maintaining multiple learning rates in parallel. This technique has been applied in the MetaGrad algorithm for online convex optimization and the Squint algorithm for prediction with expert advice. However, in both cases the user still has to provide in advance a Lipschitz hyperparameter that bounds the norm of the gradients. Although this hyperparameter is typically not available in advance, tuning it correctly is crucial: if it is set too small, the methods may fail completely; but if it is taken too large, performance deteriorates significantly. In the present work we remove this Lipschitz hyperparameter by designing new versions of MetaGrad and Squint that adapt to its optimal value automatically. We achieve this by dynamically updating the set of active learning rates. For MetaGrad, we further improve the computational efficiency of handling constraints on the domain of prediction, and we remove the need to specify the number of rounds in advance.
Dynamic Deep Multi-modal Fusion for Image Privacy Prediction
Authors: Ashwini Tonge, Cornelia Caragea
Date: 2019-02-27 00:00:00

With millions of images that are shared online on social networking sites,
effective methods for image privacy prediction are highly needed. In this
paper, we propose an approach for fusing object, scene context, and image tags
modalities derived from convolutional neural networks for accurately predicting
the privacy of images shared online. Specifically, our approach identifies the
set of most competent modalities on the fly, according to each new target image
whose privacy has to be predicted. The approach considers three stages to
predict the privacy of a target image, wherein we first identify the
neighborhood images that are visually similar and/or have similar sensitive
content as the target image. Then, we estimate the competence of the modalities
based on the neighborhood images. Finally, we fuse the decisions of the most
competent modalities and predict the privacy label for the target image.
Experimental results show that our approach predicts the sensitive (or private)
content more accurately than the models trained on individual modalities
(object, scene, and tags) and prior privacy prediction works. Also, our
approach outperforms strong baselines, that train meta-classifiers to obtain an
optimal combination of modalities.
Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images
Authors: Ruizhi Liao, Jonathan Rubin, Grace Lam, Seth Berkowitz, Sandeep Dalal, William Wells, Steven Horng, Polina Golland
Date: 2019-02-27 00:00:00
Tasks: BIG-bench Machine Learning

We propose and demonstrate machine learning algorithms to assess the severity
of pulmonary edema in chest x-ray images of congestive heart failure patients.
Accurate assessment of pulmonary edema in heart failure is critical when making
treatment and disposition decisions. Our work is grounded in a large-scale
clinical dataset of over 300,000 x-ray images with associated radiology
reports. While edema severity labels can be extracted unambiguously from a
small fraction of the radiology reports, accurate annotation is challenging in
most cases. To take advantage of the unlabeled images, we develop a Bayesian
model that includes a variational auto-encoder for learning a latent
representation from the entire image set trained jointly with a regressor that
employs this representation for predicting pulmonary edema severity. Our
experimental results suggest that modeling the distribution of images jointly
with the limited labels improves the accuracy of pulmonary edema scoring
compared to a strictly supervised approach. To the best of our knowledge, this
is the first attempt to employ machine learning algorithms to automatically and
quantitatively assess the severity of pulmonary edema in chest x-ray images.
Learning Task Knowledge and its Scope of Applicability in Experience-Based Planning Domains
Authors: Vahid Mokhtari, Luis Seabra Lopes, Armando Pinho, Roman Manevich
Date: 2019-02-27 00:00:00

Experience-based planning domains (EBPDs) have been recently proposed to
improve problem solving by learning from experience. EBPDs provide important
concepts for long-term learning and planning in robotics. They rely on
acquiring and using task knowledge, i.e., activity schemata, for generating
concrete solutions to problem instances in a class of tasks. Using Three-Valued
Logic Analysis (TVLA), we extend previous work to generate a set of conditions
as the scope of applicability for an activity schema. The inferred scope is a
bounded representation of a set of problems of potentially unbounded size, in
the form of a 3-valued logical structure, which allows an EBPD system to
automatically find an applicable activity schema for solving task problems. We
demonstrate the utility of our approach in a set of classes of problems in a
simulated domain and a class of real world tasks in a fully physically
simulated PR2 robot in Gazebo.
Semi-supervised GANs to Infer Travel Modes in GPS Trajectories
Authors: Ali Yazdizadeh, Zachary Patterson, Bilal Farooq
Date: 2019-02-27 00:00:00
Tasks: Prediction

Semi-supervised Generative Adversarial Networks (GANs) are developed in the context of travel mode inference with uni-dimensional smartphone trajectory data. We use data from a large-scale smartphone travel survey in Montreal, Canada. We convert GPS trajectories into fixed-sized segments with five channels (variables). We develop different GANs architectures and compare their prediction results with Convolutional Neural Networks (CNNs). The best semi-supervised GANs model led to a prediction accuracy of 83.4%, while the best CNN model was able to achieve the prediction accuracy of 81.3%. The results compare favorably with previous studies, especially when taking the large-scale real-world nature of the dataset into account.
Tensor Dropout for Robust Learning
Authors: Arinbjörn Kolbeinsson, Jean Kossaifi, Yannis Panagakis, Adrian Bulat, Anima Anandkumar, Ioanna Tzoulaki, Paul Matthews
Date: 2019-02-27 00:00:00
Tasks: image-classification, Image Classification, Inductive Bias

CNNs achieve remarkable performance by leveraging deep, over-parametrized architectures, trained on large datasets. However, they have limited generalization ability to data outside the training domain, and a lack of robustness to noise and adversarial attacks. By building better inductive biases, we can improve robustness and also obtain smaller networks that are more memory and computationally efficient. While standard CNNs use matrix computations, we study tensor layers that involve higher-order computations and provide better inductive bias. Specifically, we impose low-rank tensor structures on the weights of tensor regression layers to obtain compact networks, and propose tensor dropout, a randomization in the tensor rank for robustness. We show that our approach outperforms other methods for large-scale image classification on ImageNet and CIFAR-100. We establish a new state-of-the-art accuracy for phenotypic trait prediction on the largest dataset of brain MRI, the UK Biobank brain MRI dataset, where multi-linear structure is paramount. In all cases, we demonstrate superior performance and significantly improved robustness, both to noisy inputs and to adversarial attacks. We rigorously validate the theoretical validity of our approach by establishing the link between our randomized decomposition and non-linear dropout.
Insights into LSTM Fully Convolutional Networks for Time Series Classification
Authors: Fazle Karim, Somshubra Majumdar, Houshang Darabi
Date: 2019-02-27 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis, Time Series Classification

Long Short Term Memory Fully Convolutional Neural Networks (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN) have shown to achieve state-of-the-art performance on the task of classifying time series signals on the old University of California-Riverside (UCR) time series repository. However, there has been no study on why LSTM-FCN and ALSTM-FCN perform well. In this paper, we perform a series of ablation tests (3627 experiments) on LSTM-FCN and ALSTM-FCN to provide a better understanding of the model and each of its sub-module. Results from the ablation tests on ALSTM-FCN and LSTM-FCN show that the LSTM and the FCN blocks perform better when applied in a conjoined manner. Two z-normalizing techniques, z-normalizing each sample independently and z-normalizing the whole dataset, are compared using a Wilcoxson signed-rank test to show a statistical difference in performance. In addition, we provide an understanding of the impact dimension shuffle has on LSTM-FCN by comparing its performance with LSTM-FCN when no dimension shuffle is applied. Finally, we demonstrate the performance of the LSTM-FCN when the LSTM block is replaced by a GRU, basic RNN, and Dense Block.
Adversarial Attacks on Time Series
Authors: Fazle Karim, Somshubra Majumdar, Houshang Darabi
Date: 2019-02-27 00:00:00
Tasks: Classification, Dynamic Time Warping, General Classification, Time Series, Time Series Analysis, Time Series Classification

Time series classification models have been garnering significant importance
in the research community. However, not much research has been done on
generating adversarial samples for these models. These adversarial samples can
become a security concern. In this paper, we propose utilizing an adversarial
transformation network (ATN) on a distilled model to attack various time series
classification models. The proposed attack on the classification model utilizes
a distilled model as a surrogate that mimics the behavior of the attacked
classical time series classification models. Our proposed methodology is
applied onto 1-Nearest Neighbor Dynamic Time Warping (1-NN ) DTW, a Fully
Connected Network and a Fully Convolutional Network (FCN), all of which are
trained on 42 University of California Riverside (UCR) datasets. In this paper,
we show both models were susceptible to attacks on all 42 datasets. To the best
of our knowledge, such an attack on time series classification models has never
been done before. Finally, we recommend future researchers that develop time
series classification models to incorporating adversarial data samples into
their training data sets to improve resilience on adversarial samples and to
consider model robustness as an evaluative metric.
Introspection Learning
Authors: Chris R. Serrano, Michael A. Warren
Date: 2019-02-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Traditional reinforcement learning agents learn from experience, past or
present, gained through interaction with their environment. Our approach
synthesizes experience, without requiring an agent to interact with their
environment, by asking the policy directly "Are there situations X, Y, and Z,
such that in these situations you would select actions A, B, and C?" In this
paper we present Introspection Learning, an algorithm that allows for the
asking of these types of questions of neural network policies. Introspection
Learning is reinforcement learning algorithm agnostic and the states returned
may be used as an indicator of the health of the policy or to shape the policy
in a myriad of ways. We demonstrate the usefulness of this algorithm both in
the context of speeding up training and improving robustness with respect to
safety constraints.
Nonlinear Markov Random Fields Learned via Backpropagation
Authors: Mikael Brudfors, Yaël Balbastre, John Ashburner
Date: 2019-02-27 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Although convolutional neural networks (CNNs) currently dominate competitions
on image segmentation, for neuroimaging analysis tasks, more classical
generative approaches based on mixture models are still used in practice to
parcellate brains. To bridge the gap between the two, in this paper we propose
a marriage between a probabilistic generative model, which has been shown to be
robust to variability among magnetic resonance (MR) images acquired via
different imaging protocols, and a CNN. The link is in the prior distribution
over the unknown tissue classes, which are classically modelled using a Markov
random field. In this work we model the interactions among neighbouring pixels
by a type of recurrent CNN, which can encode more complex spatial interactions.
We validate our proposed model on publicly available MR data, from different
centres, and show that it generalises across imaging protocols. This result
demonstrates a successful and principled inclusion of a CNN in a generative
model, which in turn could be adapted by any probabilistic generative approach
for image segmentation.
Joint Face Detection and Facial Motion Retargeting for Multiple Faces
Authors: Bindita Chaudhuri, Noranart Vesdapunt, Baoyuan Wang
Date: 2019-02-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Face Reconstruction, Face Alignment, Face Detection, Face Model, Face Reconstruction, motion retargeting

Facial motion retargeting is an important problem in both computer graphics
and vision, which involves capturing the performance of a human face and
transferring it to another 3D character. Learning 3D morphable model (3DMM)
parameters from 2D face images using convolutional neural networks is common in
2D face alignment, 3D face reconstruction etc. However, existing methods either
require an additional face detection step before retargeting or use a cascade
of separate networks to perform detection followed by retargeting in a
sequence. In this paper, we present a single end-to-end network to jointly
predict the bounding box locations and 3DMM parameters for multiple faces.
First, we design a novel multitask learning framework that learns a
disentangled representation of 3DMM parameters for a single face. Then, we
leverage the trained single face model to generate ground truth 3DMM parameters
for multiple faces to train another network that performs joint face detection
and motion retargeting for images with multiple faces. Experimental results
show that our joint detection and retargeting network has high face detection
accuracy and is robust to extreme expressions and poses while being faster than
state-of-the-art methods.
Object-driven Text-to-Image Synthesis via Adversarial Training
Authors: Wenbo Li, Pengchuan Zhang, Lei Zhang, Qiuyuan Huang, Xiaodong He, Siwei Lyu, Jianfeng Gao
Date: 2019-02-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Generation, Object

In this paper, we propose Object-driven Attentive Generative Adversarial
Newtorks (Obj-GANs) that allow object-centered text-to-image synthesis for
complex scenes. Following the two-step (layout-image) generation process, a
novel object-driven attentive image generator is proposed to synthesize salient
objects by paying attention to the most relevant words in the text description
and the pre-generated semantic layout. In addition, a new Fast R-CNN based
object-wise discriminator is proposed to provide rich object-wise
discrimination signals on whether the synthesized object matches the text
description and the pre-generated layout. The proposed Obj-GAN significantly
outperforms the previous state of the art in various metrics on the large-scale
COCO benchmark, increasing the Inception score by 27% and decreasing the FID
score by 11%. A thorough comparison between the traditional grid attention and
the new object-driven attention is provided through analyzing their mechanisms
and visualizing their attention layers, showing insights of how the proposed
model generates complex scenes in high quality.
A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images
Authors: John Leuner
Date: 2019-02-27 00:00:00
Tasks: BIG-bench Machine Learning

Recent research used machine learning methods to predict a person's sexual
orientation from their photograph (Wang and Kosinski, 2017). To verify this
result, two of these models are replicated, one based on a deep neural network
(DNN) and one on facial morphology (FM). Using a new dataset of 20,910
photographs from dating websites, the ability to predict sexual orientation is
confirmed (DNN accuracy male 68%, female 77%, FM male 62%, female 72%). To
investigate whether facial features such as brightness or predominant colours
are predictive of sexual orientation, a new model based on highly blurred
facial images was created. This model was also able to predict sexual
orientation (male 63%, female 72%). The tested models are invariant to
intentional changes to a subject's makeup, eyewear, facial hair and head pose
(angle that the photograph is taken at). It is shown that the head pose is not
correlated with sexual orientation. While demonstrating that dating profile
images carry rich information about sexual orientation these results leave open
the question of how much is determined by facial morphology and how much by
differences in grooming, presentation and lifestyle. The advent of new
technology that is able to detect sexual orientation in this way may have
serious implications for the privacy and safety of gay men and women.