pwc-archive/papers-with-abstracts huggingface dataset:
On the Limitation of MagNet Defense against $L_1$-based Adversarial Examples
Authors: Pei-Hsuan Lu, Pin-Yu Chen, Kang-Cheng Chen, Chia-Mu Yu
Date: 2018-04-14 00:00:00

In recent years, defending adversarial perturbations to natural examples in
order to build robust machine learning models trained by deep neural networks
(DNNs) has become an emerging research field in the conjunction of deep
learning and security. In particular, MagNet consisting of an adversary
detector and a data reformer is by far one of the strongest defenses in the
black-box oblivious attack setting, where the attacker aims to craft
transferable adversarial examples from an undefended DNN model to bypass an
unknown defense module deployed on the same DNN model. Under this setting,
MagNet can successfully defend a variety of attacks in DNNs, including the
high-confidence adversarial examples generated by the Carlini and Wagner's
attack based on the $L_2$ distortion metric. However, in this paper, under the
same attack setting we show that adversarial examples crafted based on the
$L_1$ distortion metric can easily bypass MagNet and mislead the target DNN
image classifiers on MNIST and CIFAR-10. We also provide explanations on why
the considered approach can yield adversarial examples with superior attack
performance and conduct extensive experiments on variants of MagNet to verify
its lack of robustness to $L_1$ distortion based attacks. Notably, our results
substantially weaken the assumption of effective threat models on MagNet that
require knowing the deployed defense technique when attacking DNNs (i.e., the
gray-box attack setting).
Policy Optimization with Second-Order Advantage Information
Authors: Jiajin Li, Baoxiang Wang
Date: 2018-05-09 00:00:00
Tasks: continuous-control, Continuous Control, MuJoCo

Policy optimization on high-dimensional continuous control tasks exhibits its difficulty caused by the large variance of the policy gradient estimators. We present the action subspace dependent gradient (ASDG) estimator which incorporates the Rao-Blackwell theorem (RB) and Control Variates (CV) into a unified framework to reduce the variance. To invoke RB, our proposed algorithm (POSA) learns the underlying factorization structure among the action space based on the second-order advantage information. POSA captures the quadratic information explicitly and efficiently by utilizing the wide & deep architecture. Empirical studies show that our proposed approach demonstrates the performance improvements on high-dimensional synthetic settings and OpenAI Gym's MuJoCo continuous control tasks.
End-to-End Polyphonic Sound Event Detection Using Convolutional Recurrent Neural Networks with Learned Time-Frequency Representation Input
Authors: Emre Çakır, Tuomas Virtanen
Date: 2018-05-09 00:00:00
Tasks: Event Detection, Sound Event Detection

Sound event detection systems typically consist of two stages: extracting
hand-crafted features from the raw audio waveform, and learning a mapping
between these features and the target sound events using a classifier.
Recently, the focus of sound event detection research has been mostly shifted
to the latter stage using standard features such as mel spectrogram as the
input for classifiers such as deep neural networks. In this work, we utilize
end-to-end approach and propose to combine these two stages in a single deep
neural network classifier. The feature extraction over the raw waveform is
conducted by a feedforward layer block, whose parameters are initialized to
extract the time-frequency representations. The feature extraction parameters
are updated during training, resulting with a representation that is optimized
for the specific task. This feature extraction block is followed by (and
jointly trained with) a convolutional recurrent network, which has recently
given state-of-the-art results in many sound recognition tasks. The proposed
system does not outperform a convolutional recurrent network with fixed
hand-crafted features. The final magnitude spectrum characteristics of the
feature extraction block parameters indicate that the most relevant information
for the given task is contained in 0 - 3 kHz frequency range, and this is also
supported by the empirical results on the SED performance.
Ensemble Multi-task Gaussian Process Regression with Multiple Latent Processes
Authors: Weitong Ruan, Eric L. Miller
Date: 2017-09-22 00:00:00
Tasks: Gaussian Processes, regression

Multi-task/Multi-output learning seeks to exploit correlation among tasks to
enhance performance over learning or solving each task independently. In this
paper, we investigate this problem in the context of Gaussian Processes (GPs)
and propose a new model which learns a mixture of latent processes by
decomposing the covariance matrix into a sum of structured hidden components
each of which is controlled by a latent GP over input features and a "weight"
over tasks. From this sum structure, we propose a parallelizable parameter
learning algorithm with a predetermined initialization for the "weights". We
also notice that an ensemble parameter learning approach using mini-batches of
training data not only reduces the computation complexity of learning but also
improves the regression performance. We evaluate our model on two datasets, the
smaller Swiss Jura dataset and another relatively larger ATMS dataset from
NOAA. Substantial improvements are observed compared with established
alternatives.
End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching
Authors: Georgios Georgakis, Srikrishna Karanam, Ziyan Wu, Jan Ernst, Jana Kosecka
Date: 2018-02-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Retrieval, Keypoint Detection, Metric Learning, Retrieval

Finding correspondences between images or 3D scans is at the heart of many
computer vision and image retrieval applications and is often enabled by
matching local keypoint descriptors. Various learning approaches have been
applied in the past to different stages of the matching pipeline, considering
detector, descriptor, or metric learning objectives. These objectives were
typically addressed separately and most previous work has focused on image
data. This paper proposes an end-to-end learning framework for keypoint
detection and its representation (descriptor) for 3D depth maps or 3D scans,
where the two can be jointly optimized towards task-specific objectives without
a need for separate annotations. We employ a Siamese architecture augmented by
a sampling layer and a novel score loss function which in turn affects the
selection of region proposals. The positive and negative examples are obtained
automatically by sampling corresponding region proposals based on their
consistency with known 3D pose labels. Matching experiments with depth data on
multiple benchmark datasets demonstrate the efficacy of the proposed approach,
showing significant improvements over state-of-the-art methods.
Near-drowning Early Prediction Technique Using Novel Equations (NEPTUNE) for Swimming Pools
Authors: Bhaskaran David Prakash
Date: 2018-05-07 00:00:00
Tasks: Clustering

Safety is a critical aspect in all swimming pools. This paper describes a
near drowning early prediction technique using novel equations (NEPTUNE).
NEPTUNE uses equations or rules that would be able to detect near drowning
using at least 1 but not more than 5 seconds of video sequence with no false
positives. The backbone of NEPTUNE encompasses a mix of statistical image
processing to merge images for a video sequence followed by K means clustering
to extract segments in the merged image and finally a revisit to statistical
image processing to derive variables for every segment. These variables would
be used by the equations to identify near-drowning. NEPTUNE has the potential
to be integrated into a swimming pool camera system that would send an alarm to
the lifeguards for early response so that the likelihood of recovery is high.
Video Object Segmentation with Language Referring Expressions
Authors: Anna Khoreva, Anna Rohrbach, Bernt Schiele
Date: 2018-03-21 00:00:00
Tasks: Object, Referring Expression Segmentation, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Most state-of-the-art semi-supervised video object segmentation methods rely
on a pixel-accurate mask of a target object provided for the first frame of a
video. However, obtaining a detailed segmentation mask is expensive and
time-consuming. In this work we explore an alternative way of identifying a
target object, namely by employing language referring expressions. Besides
being a more practical and natural way of pointing out a target object, using
language specifications can help to avoid drift as well as make the system more
robust to complex dynamics and appearance variations. Leveraging recent
advances of language grounding models designed for images, we propose an
approach to extend them to video data, ensuring temporally coherent
predictions. To evaluate our method we augment the popular video object
segmentation benchmarks, DAVIS'16 and DAVIS'17 with language descriptions of
target objects. We show that our language-supervised approach performs on par
with the methods which have access to a pixel-level mask of the target object
on DAVIS'16 and is competitive to methods using scribbles on the challenging
DAVIS'17 dataset.
On Visual Hallmarks of Robustness to Adversarial Malware
Authors: Alex Huang, Abdullah Al-Dujaili, Erik Hemberg, Una-May O'Reilly
Date: 2018-05-09 00:00:00

A central challenge of adversarial learning is to interpret the resulting
hardened model. In this contribution, we ask how robust generalization can be
visually discerned and whether a concise view of the interactions between a
hardened decision map and input samples is possible. We first provide a means
of visually comparing a hardened model's loss behavior with respect to the
adversarial variants generated during training versus loss behavior with
respect to adversarial variants generated from other sources. This allows us to
confirm that the association of observed flatness of a loss landscape with
generalization that is seen with naturally trained models extends to
adversarially hardened models and robust generalization. To complement these
means of interpreting model parameter robustness we also use self-organizing
maps to provide a visual means of superimposing adversarial and natural
variants on a model's decision space, thus allowing the model's global
robustness to be comprehensively examined.
High-resolution medical image synthesis using progressively grown generative adversarial networks
Authors: Andrew Beers, James Brown, Ken Chang, J. Peter Campbell, Susan Ostmo, Michael F. Chiang, Jayashree Kalpathy-Cramer
Date: 2018-05-08 00:00:00
Tasks: Image Augmentation, Image Generation, Vocal Bursts Intensity Prediction

Generative adversarial networks (GANs) are a class of unsupervised machine
learning algorithms that can produce realistic images from randomly-sampled
vectors in a multi-dimensional space. Until recently, it was not possible to
generate realistic high-resolution images using GANs, which has limited their
applicability to medical images that contain biomarkers only detectable at
native resolution. Progressive growing of GANs is an approach wherein an image
generator is trained to initially synthesize low resolution synthetic images
(8x8 pixels), which are then fed to a discriminator that distinguishes these
synthetic images from real downsampled images. Additional convolutional layers
are then iteratively introduced to produce images at twice the previous
resolution until the desired resolution is reached. In this work, we
demonstrate that this approach can produce realistic medical images in two
different domains; fundus photographs exhibiting vascular pathology associated
with retinopathy of prematurity (ROP), and multi-modal magnetic resonance
images of glioma. We also show that fine-grained details associated with
pathology, such as retinal vessels or tumor heterogeneity, can be preserved and
enhanced by including segmentation maps as additional channels. We envisage
several applications of the approach, including image augmentation and
unsupervised classification of pathology.
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
Authors: Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz
Date: 2018-02-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Part Segmentation, 3D Semantic Segmentation, Semantic Segmentation

We present a network architecture for processing point clouds that directly
operates on a collection of points represented as a sparse set of samples in a
high-dimensional lattice. Naively applying convolutions on this lattice scales
poorly, both in terms of memory and computational cost, as the size of the
lattice increases. Instead, our network uses sparse bilateral convolutional
layers as building blocks. These layers maintain efficiency by using indexing
structures to apply convolutions only on occupied parts of the lattice, and
allow flexible specifications of the lattice structure enabling hierarchical
and spatially-aware feature learning, as well as joint 2D-3D reasoning. Both
point-based and image-based representations can be easily incorporated in a
network with such layers and the resulting model can be trained in an
end-to-end manner. We present results on 3D segmentation tasks where our
approach outperforms existing state-of-the-art techniques.
Solving Sudoku with Ant Colony Optimisation
Authors: Huw Lloyd, Martyn Amos
Date: 2018-05-09 00:00:00

In this paper we present a new Ant Colony Optimisation-based algorithm for
Sudoku, which out-performs existing methods on large instances. Our method
includes a novel anti-stagnation operator, which we call Best Value
Evaporation.
Reference-less Measure of Faithfulness for Grammatical Error Correction
Authors: Leshem Choshen, Omri Abend
Date: 2018-04-11 00:00:00
Proceeding: NAACL 2018 6
Tasks: Grammatical Error Correction, valid

We propose USim, a semantic measure for Grammatical Error Correction (GEC)
that measures the semantic faithfulness of the output to the source, thereby
complementing existing reference-less measures (RLMs) for measuring the
output's grammaticality. USim operates by comparing the semantic symbolic
structure of the source and the correction, without relying on manually-curated
references. Our experiments establish the validity of USim, by showing that (1)
semantic annotation can be consistently applied to ungrammatical text; (2)
valid corrections obtain a high USim similarity score to the source; and (3)
invalid corrections obtain a lower score.
TensorLy: Tensor Learning in Python
Authors: Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic
Date: 2016-10-29 00:00:00
Tasks: CPU, GPU

Tensors are higher-order extensions of matrices. While matrix methods form
the cornerstone of machine learning and data analysis, tensor methods have been
gaining increasing traction. However, software support for tensor operations is
not on the same footing. In order to bridge this gap, we have developed
\emph{TensorLy}, a high-level API for tensor methods and deep tensorized neural
networks in Python. TensorLy aims to follow the same standards adopted by the
main projects of the Python scientific community, and seamlessly integrates
with them. Its BSD license makes it suitable for both academic and commercial
applications. TensorLy's backend system allows users to perform computations
with NumPy, MXNet, PyTorch, TensorFlow and CuPy. They can be scaled on multiple
CPU or GPU machines. In addition, using the deep-learning frameworks as backend
allows users to easily design and train deep tensorized neural networks.
TensorLy is available at https://github.com/tensorly/tensorly
FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation
Authors: René Schuster, Christian Bailer, Oliver Wasenmüller, Didier Stricker
Date: 2018-05-09 00:00:00
Tasks: Optical Flow Estimation

Optical Flow algorithms are of high importance for many applications.
Recently, the Flow Field algorithm and its modifications have shown remarkable
results, as they have been evaluated with top accuracy on different data sets.
In our analysis of the algorithm we have found that it produces accurate sparse
matches, but there is room for improvement in the interpolation. Thus, we
propose in this paper FlowFields++, where we combine the accurate matches of
Flow Fields with a robust interpolation. In addition, we propose improved
variational optimization as post-processing. Our new algorithm is evaluated on
the challenging KITTI and MPI Sintel data sets with public top results on both
benchmarks.
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images
Authors: Qian Yu, Yinghuan Shi, Jinquan Sun, Yang Gao, Yakang Dai, Jianbing Zhu
Date: 2018-04-27 00:00:00
Tasks: Cardiac Segmentation, Segmentation, Tumor Segmentation

Due to the irregular motion, similar appearance and diverse shape, accurate segmentation of kidney tumor in CT images is a difficult and challenging task. To this end, we present a novel automatic segmentation method, termed as Crossbar-Net, with the goal of accurate segmenting the kidney tumors. Firstly, considering that the traditional learning-based segmentation methods normally employ either whole images or squared patches as the training samples, we innovatively sample the orthogonal non-squared patches (namely crossbar patches), to fully cover the whole kidney tumors in either horizontal or vertical directions. These sampled crossbar patches could not only represent the detailed local information of kidney tumor as the traditional patches, but also describe the global appearance from either horizontal or vertical direction using contextual information. Secondly, with the obtained crossbar patches, we trained a convolutional neural network with two sub-models (i.e., horizontal sub-model and vertical sub-model) in a cascaded manner, to integrate the segmentation results from two directions (i.e., horizontal and vertical). This cascaded training strategy could effectively guarantee the consistency between sub-models, by feeding each other with the most difficult samples, for a better segmentation. In the experiment, we evaluate our method on a real CT kidney tumor dataset, collected from 94 different patients including 3,500 images. Compared with the state-of-the-art segmentation methods, the results demonstrate the superior results of our method on dice ratio score, true positive fraction, centroid distance and Hausdorff distance. Moreover, we have extended our crossbar-net to a different task: cardiac segmentation, showing the promising results for the better generalization.
Rethinking Diversified and Discriminative Proposal Generation for Visual Grounding
Authors: Zhou Yu, Jun Yu, Chenchao Xiang, Zhou Zhao, Qi Tian, DaCheng Tao
Date: 2018-05-09 00:00:00
Tasks: Diversity, Phrase Grounding, Visual Grounding

Visual grounding aims to localize an object in an image referred to by a
textual query phrase. Various visual grounding approaches have been proposed,
and the problem can be modularized into a general framework: proposal
generation, multi-modal feature representation, and proposal ranking. Of these
three modules, most existing approaches focus on the latter two, with the
importance of proposal generation generally neglected. In this paper, we
rethink the problem of what properties make a good proposal generator. We
introduce the diversity and discrimination simultaneously when generating
proposals, and in doing so propose Diversified and Discriminative Proposal
Networks model (DDPN). Based on the proposals generated by DDPN, we propose a
high performance baseline model for visual grounding and evaluate it on four
benchmark datasets. Experimental results demonstrate that our model delivers
significant improvements on all the tested data-sets (e.g., 18.8\% improvement
on ReferItGame and 8.2\% improvement on Flickr30k Entities over the existing
state-of-the-arts respectively)
Joint Action Unit localisation and intensity estimation through heatmap regression
Authors: Enrique Sanchez-Lozano, Georgios Tzimiropoulos, Michel Valstar
Date: 2018-05-09 00:00:00
Tasks: regression

This paper proposes a supervised learning approach to jointly perform facial
Action Unit (AU) localisation and intensity estimation. Contrary to previous
works that try to learn an unsupervised representation of the Action Unit
regions, we propose to directly and jointly estimate all AU intensities through
heatmap regression, along with the location in the face where they cause
visible changes. Our approach aims to learn a pixel-wise regression function
returning a score per AU, which indicates an AU intensity at a given spatial
location. Heatmap regression then generates an image, or channel, per AU, in
which each pixel indicates the corresponding AU intensity. To generate the
ground-truth heatmaps for a target AU, the facial landmarks are first
estimated, and a 2D Gaussian is drawn around the points where the AU is known
to cause changes. The amplitude and size of the Gaussian is determined by the
intensity of the AU. We show that using a single Hourglass network suffices to
attain new state of the art results, demonstrating the effectiveness of such a
simple approach. The use of heatmap regression allows learning of a shared
representation between AUs without the need to rely on latent representations,
as these are implicitly learned from the data. We validate the proposed
approach on the BP4D dataset, showing a modest improvement on recent, complex,
techniques, as well as robustness against misalignment errors. Code for testing
and models will be available to download from
https://github.com/ESanchezLozano/Action-Units-Heatmaps.
Adaptive User-Oriented Direct Load-Control of Residential Flexible Devices
Authors: Davide Frazzetto, Bijay Neupane, Torben Bach Pedersen, Thomas Dyhre Nielsen
Date: 2018-05-09 00:00:00
Tasks: Scheduling

Demand Response (DR) schemes are effective tools to maintain a dynamic
balance in energy markets with higher integration of fluctuating renewable
energy sources. DR schemes can be used to harness residential devices'
flexibility and to utilize it to achieve social and financial objectives.
However, existing DR schemes suffer from low user participation as they fail at
taking into account the users' requirements. First, DR schemes are highly
demanding for the users, as users need to provide direct information, e.g. via
surveys, on their energy consumption preferences. Second, the user utility
models based on these surveys are hard-coded and do not adapt over time. Third,
the existing scheduling techniques require the users to input their energy
requirements on a daily basis. As an alternative, this paper proposes a DR
scheme for user-oriented direct load-control of residential appliances
operations. Instead of relying on user surveys to evaluate the user utility, we
propose an online data-driven approach for estimating user utility functions,
purely based on available load consumption data, that adaptively models the
users' preference over time. Our scheme is based on a day-ahead scheduling
technique that transparently prescribes the users with optimal device operation
schedules that take into account both financial benefits and user-perceived
quality of service. To model day-ahead user energy demand and flexibility, we
propose a probabilistic approach for generating flexibility models under
uncertainty. Results on both real-world and simulated datasets show that our DR
scheme can provide significant financial benefits while preserving the
user-perceived quality of service.