pwc-archive/papers-with-abstracts huggingface dataset:
Feature selection with optimal coordinate ascent (OCA)
Authors: David Saltiel, Eric Benhamou
Date: 2018-11-29 00:00:00
Tasks: feature selection

In machine learning, Feature Selection (FS) is a major part of efficient
algorithm. It fuels the algorithm and is the starting block for our prediction.
In this paper, we present a new method, called Optimal Coordinate Ascent (OCA)
that allows us selecting features among block and individual features. OCA
relies on coordinate ascent to find an optimal solution for gradient boosting
methods score (number of correctly classified samples). OCA takes into account
the notion of dependencies between variables forming blocks in our
optimization. The coordinate ascent optimization solves the issue of the NP
hard original problem where the number of combinations rapidly explode making a
grid search unfeasible. It reduces considerably the number of iterations
changing this NP hard problem into a polynomial search one. OCA brings
substantial differences and improvements compared to previous coordinate ascent
feature selection method: we group variables into block and individual
variables instead of a binary selection. Our initial guess is based on the
k-best group variables making our initial point more robust. We also introduced
new stopping criteria making our optimization faster. We compare these two
methods on our data set. We found that our method outperforms the initial one.
We also compare our method to the Recursive Feature Elimination (RFE) method
and find that OCA leads to the minimum feature set with the highest score. This
is a nice byproduct of our method as it provides empirically the most compact
data set with optimal performance.
Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series
Authors: Daniel J. Trosten, Andreas S. Strauman, Michael Kampffmeyer, Robert Jenssen
Date: 2018-11-29 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

The task of clustering unlabeled time series and sequences entails a
particular set of challenges, namely to adequately model temporal relations and
variable sequence lengths. If these challenges are not properly handled, the
resulting clusters might be of suboptimal quality. As a key solution, we
present a joint clustering and feature learning framework for time series based
on deep learning. For a given set of time series, we train a recurrent network
to represent, or embed, each time series in a vector space such that a
divergence-based clustering loss function can discover the underlying cluster
structure in an end-to-end manner. Unlike previous approaches, our model
inherently handles multivariate time series of variable lengths and does not
require specification of a distance-measure in the input space. On a diverse
set of benchmark datasets we illustrate that our proposed Recurrent Deep
Divergence-based Clustering approach outperforms, or performs comparable to,
previous approaches.
Generalized Coarse-to-Fine Visual Recognition with Progressive Training
Authors: Xutong Ren, Lingxi Xie, Chen Wei, Siyuan Qiao, Chi Su, Jiaying Liu, Qi Tian, Elliot K. Fishman, Alan L. Yuille
Date: 2018-11-29 00:00:00
Tasks: image-classification, Image Classification, Object Localization, Semantic Segmentation

Computer vision is difficult, partly because the desired mathematical
function connecting input and output data is often complex, fuzzy and thus hard
to learn. Coarse-to-fine (C2F) learning is a promising direction, but it
remains unclear how it is applied to a wide range of vision problems.
  This paper presents a generalized C2F framework by making two technical
contributions. First, we provide a unified way of C2F propagation, in which the
coarse prediction (a class vector, a detected box, a segmentation mask, etc.)
is encoded into a dense (pixel-level) matrix and concatenated to the original
input, so that the fine model takes the same design of the coarse model but
sees additional information. Second, we present a progressive training strategy
which starts with feeding the ground-truth instead of the coarse output into
the fine model, and gradually increases the fraction of coarse output, so that
at the end of training the fine model is ready for testing. We also relate our
approach to curriculum learning by showing that data difficulty keeps
increasing during the training process. We apply our framework to three vision
tasks including image classification, object localization and semantic
segmentation, and demonstrate consistent accuracy gain compared to the baseline
training strategy.
A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods
Authors: David Charte, Francisco Charte, Salvador García, Francisco Herrera
Date: 2018-11-29 00:00:00
Tasks: Binary Classification, Classification, Clustering, General Classification, regression

Machine learning is a field which studies how machines can alter and adapt
their behavior, improving their actions according to the information they are
given. This field is subdivided into multiple areas, among which the best known
are supervised learning (e.g. classification and regression) and unsupervised
learning (e.g. clustering and association rules).
  Within supervised learning, most studies and research are focused on well
known standard tasks, such as binary classification, multiclass classification
and regression with one dependent variable. However, there are many other less
known problems. These are what we generically call nonstandard supervised
learning problems. The literature about them is much more sparse, and each
study is directed to a specific task. Therefore, the definitions, relations and
applications of this kind of learners are hard to find.
  The goal of this paper is to provide the reader with a broad view on the
distinct variations of nonstandard supervised problems. A comprehensive
taxonomy summarizing their traits is proposed. A review of the common
approaches followed to accomplish them and their main applications is provided
as well.
MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution
Authors: Jun-Hyuk Kim, Jun-Ho Choi, Manri Cheon, Jong-Seok Lee
Date: 2018-11-29 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

In recent years, single image super-resolution (SR) methods based on deep convolutional neural networks (CNNs) have made significant progress. However, due to the non-adaptive nature of the convolution operation, they cannot adapt to various characteristics of images, which limits their representational capability and, consequently, results in unnecessarily large model sizes. To address this issue, we propose a novel multi-path adaptive modulation network (MAMNet). Specifically, we propose a multi-path adaptive modulation block (MAMB), which is a lightweight yet effective residual block that adaptively modulates residual feature responses by fully exploiting their information via three paths. The three paths model three types of information suitable for SR: 1) channel-specific information (CSI) using global variance pooling, 2) inter-channel dependencies (ICD) based on the CSI, 3) and channel-specific spatial dependencies (CSD) via depth-wise convolution. We demonstrate that the proposed MAMB is effective and parameter-efficient for image SR than other feature modulation methods. In addition, experimental results show that our MAMNet outperforms most of the state-of-the-art methods with a relatively small number of parameters.
The Power of The Hybrid Model for Mean Estimation
Authors: Brendan Avent, Yatharth Dubey, Aleksandra Korolova
Date: 2018-11-29 00:00:00

We explore the power of the hybrid model of differential privacy (DP), in which some users desire the guarantees of the local model of DP and others are content with receiving the trusted-curator model guarantees. In particular, we study the utility of hybrid model estimators that compute the mean of arbitrary real-valued distributions with bounded support. When the curator knows the distribution's variance, we design a hybrid estimator that, for realistic datasets and parameter settings, achieves a constant factor improvement over natural baselines. We then analytically characterize how the estimator's utility is parameterized by the problem setting and parameter choices. When the distribution's variance is unknown, we design a heuristic hybrid estimator and analyze how it compares to the baselines. We find that it often performs better than the baselines, and sometimes almost as well as the known-variance estimator. We then answer the question of how our estimator's utility is affected when users' data are not drawn from the same distribution, but rather from distributions dependent on their trust model preference. Concretely, we examine the implications of the two groups' distributions diverging and show that in some cases, our estimators maintain fairly high utility. We then demonstrate how our hybrid estimator can be incorporated as a sub-component in more complex, higher-dimensional applications. Finally, we propose a new privacy amplification notion for the hybrid model that emerges due to interaction between the groups, and derive corresponding amplification results for our hybrid estimators.
EV-SegNet: Semantic Segmentation for Event-based Cameras
Authors: Iñigo Alonso, Ana C. Murillo
Date: 2018-11-29 00:00:00
Tasks: 3D Reconstruction, Segmentation, Semantic Segmentation, valid

Event cameras, or Dynamic Vision Sensor (DVS), are very promising sensors
which have shown several advantages over frame based cameras. However, most
recent work on real applications of these cameras is focused on 3D
reconstruction and 6-DOF camera tracking. Deep learning based approaches, which
are leading the state-of-the-art in visual recognition tasks, could potentially
take advantage of the benefits of DVS, but some adaptations are needed still
needed in order to effectively work on these cameras. This work introduces a
first baseline for semantic segmentation with this kind of data. We build a
semantic segmentation CNN based on state-of-the-art techniques which takes
event information as the only input. Besides, we propose a novel representation
for DVS data that outperforms previously used event representations for related
tasks. Since there is no existing labeled dataset for this task, we propose how
to automatically generate approximated semantic segmentation labels for some
sequences of the DDD17 dataset, which we publish together with the model, and
demonstrate they are valid to train a model for DVS data only. We compare our
results on semantic segmentation from DVS data with results using corresponding
grayscale images, demonstrating how they are complementary and worth combining.
Utilizing Complex-valued Network for Learning to Compare Image Patches
Authors: Siwen Jiang, Wenxuan Wei, Shihao Guo, Hongguang Fu, Lei Huang
Date: 2018-11-29 00:00:00
Tasks: Metric Learning

At present, the great achievements of convolutional neural network(CNN) in
feature and metric learning have attracted many researchers. However, the vast
majority of deep network architectures have been used to represent based on
real values. The research of complex-valued networks is seldom concerned due to
the absence of effective models and suitable distance of complex-valued vector.
Motived by recent works, complex vectors have been shown to have a richer
representational capacity and efficient complex blocks have been reported, we
propose a new approach for learning image descriptors with complex numbers to
compare image patches. We also propose a new architecture to learn image
similarity function directly based on complex-valued network. We show that our
models can perform competitive results on benchmark datasets. We make the
source code of our models publicly available.
Grid R-CNN
Authors: Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan
Date: 2018-11-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: 2D Object Detection, Novel Object Detection, Object, object-detection, Object Detection, Object Localization

This paper proposes a novel object detection framework named Grid R-CNN,
which adopts a grid guided localization mechanism for accurate object
detection. Different from the traditional regression based methods, the Grid
R-CNN captures the spatial information explicitly and enjoys the position
sensitive property of fully convolutional architecture. Instead of using only
two independent points, we design a multi-point supervision formulation to
encode more clues in order to reduce the impact of inaccurate prediction of
specific points. To take the full advantage of the correlation of points in a
grid, we propose a two-stage information fusion strategy to fuse feature maps
of neighbor grid points. The grid guided localization approach is easy to be
extended to different state-of-the-art detection frameworks. Grid R-CNN leads
to high quality object localization, and experiments demonstrate that it
achieves a 4.1% AP gain at IoU=0.8 and a 10.0% AP gain at IoU=0.9 on COCO
benchmark compared to Faster R-CNN with Res50 backbone and FPN architecture.
MOBIUS: Model-Oblivious Binarized Neural Networks
Authors: Hiromasa Kitai, Jason Paul Cruz, Naoto Yanai, Naohisa Nishida, Tatsumi Oba, Yuji Unagami, Tadanori Teruya, Nuttapong Attrapadung, Takahiro Matsuda, Goichiro Hanaoka
Date: 2018-11-29 00:00:00
Tasks: BIG-bench Machine Learning, model, Prediction, Privacy Preserving

A privacy-preserving framework in which a computational resource provider
receives encrypted data from a client and returns prediction results without
decrypting the data, i.e., oblivious neural network or encrypted prediction,
has been studied in machine learning that provides prediction services. In this
work, we present MOBIUS (Model-Oblivious BInary neUral networkS), a new system
that combines Binarized Neural Networks (BNNs) and secure computation based on
secret sharing as tools for scalable and fast privacy-preserving machine
learning. BNNs improve computational performance by binarizing values in
training to $-1$ and $+1$, while secure computation based on secret sharing
provides fast and various computations under encrypted forms via modulo
operations with a short bit length. However, combining these tools is not
trivial because their operations have different algebraic structures and the
use of BNNs downgrades prediction accuracy in general. MOBIUS uses improved
procedures of BNNs and secure computation that have compatible algebraic
structures without downgrading prediction accuracy. We created an
implementation of MOBIUS in C++ using the ABY library (NDSS 2015). We then
conducted experiments using the MNIST dataset, and the results show that MOBIUS
can return a prediction within 0.76 seconds, which is six times faster than
SecureML (IEEE S\&P 2017). MOBIUS allows a client to request for encrypted
prediction and allows a trainer to obliviously publish an encrypted model to a
cloud provided by a computational resource provider, i.e., without revealing
the original model itself to the provider.
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network
Authors: Qing Song, Yingqi Wu, Lu Yang
Date: 2018-11-29 00:00:00
Tasks: Adversarial Attack, Face Recognition

With the broad use of face recognition, its weakness gradually emerges that
it is able to be attacked. So, it is important to study how face recognition
networks are subject to attacks. In this paper, we focus on a novel way to do
attacks against face recognition network that misleads the network to identify
someone as the target person not misclassify inconspicuously. Simultaneously,
for this purpose, we introduce a specific attentional adversarial attack
generative network to generate fake face images. For capturing the semantic
information of the target person, this work adds a conditional variational
autoencoder and attention modules to learn the instance-level correspondences
between faces. Unlike traditional two-player GAN, this work introduces face
recognition networks as the third player to participate in the competition
between generator and discriminator which allows the attacker to impersonate
the target person better. The generated faces which are hard to arouse the
notice of onlookers can evade recognition by state-of-the-art networks and most
of them are recognized as the target person.
Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks
Authors: Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka
Date: 2018-11-29 00:00:00
Proceeding: CVPR 2019 6

Large-scale distributed training of deep neural networks suffer from the
generalization gap caused by the increase in the effective mini-batch size.
Previous approaches try to solve this problem by varying the learning rate and
batch size over epochs and layers, or some ad hoc modification of the batch
normalization. We propose an alternative approach using a second-order
optimization method that shows similar generalization capability to first-order
methods, but converges faster and can handle larger mini-batches. To test our
method on a benchmark where highly optimized first-order methods are available
as references, we train ResNet-50 on ImageNet. We converged to 75% Top-1
validation accuracy in 35 epochs for mini-batch sizes under 16,384, and
achieved 75% even with a mini-batch size of 131,072, which took only 978
iterations.
3D Shape Reconstruction from a Single 2D Image via 2D-3D Self-Consistency
Authors: Yi-Lun Liao, Yao-Cheng Yang, Yu-Chiang Frank Wang
Date: 2018-11-29 00:00:00
Tasks: 3D Reconstruction, 3D Shape Reconstruction, 3D Shape Reconstruction From A Single 2D Image, Foreground Segmentation

Aiming at inferring 3D shapes from 2D images, 3D shape reconstruction has
drawn huge attention from researchers in computer vision and deep learning
communities. However, it is not practical to assume that 2D input images and
their associated ground truth 3D shapes are always available during training.
In this paper, we propose a framework for semi-supervised 3D reconstruction.
This is realized by our introduced 2D-3D self-consistency, which aligns the
predicted 3D models and the projected 2D foreground segmentation masks.
Moreover, our model not only enables recovering 3D shapes with the
corresponding 2D masks, camera pose information can be jointly disentangled and
predicted, even such supervision is never available during training. In the
experiments, we qualitatively and quantitatively demonstrate the effectiveness
of our model, which performs favorably against state-of-the-art approaches in
either supervised or semi-supervised settings.
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression
Authors: Xiang Gao, Wei Hu, Jiaxiang Tang, Jiaying Liu, Zongming Guo
Date: 2018-11-29 00:00:00
Tasks: Action Recognition, graph construction, Graph Regression, regression, Skeleton Based Action Recognition, Temporal Action Localization

With the prevalence of accessible depth sensors, dynamic human body skeletons
have attracted much attention as a robust modality for action recognition.
Previous methods model skeletons based on RNN or CNN, which has limited
expressive power for irregular skeleton joints. While graph convolutional
networks (GCN) have been proposed to address irregular graph-structured data,
the fundamental graph construction remains challenging. In this paper, we
represent skeletons naturally on graphs, and propose a graph regression based
GCN (GR-GCN) for skeleton-based action recognition, aiming to capture the
spatio-temporal variation in the data. As the graph representation is crucial
to graph convolution, we first propose graph regression to statistically learn
the underlying graph from multiple observations. In particular, we provide
spatio-temporal modeling of skeletons and pose an optimization problem on the
graph structure over consecutive frames, which enforces the sparsity of the
underlying graph for efficient representation. The optimized graph not only
connects each joint to its neighboring joints in the same frame strongly or
weakly, but also links with relevant joints in the previous and subsequent
frames. We then feed the optimized graph into the GCN along with the
coordinates of the skeleton sequence for feature learning, where we deploy
high-order and fast Chebyshev approximation of spectral graph convolution.
Further, we provide analysis of the variation characterization by the Chebyshev
approximation. Experimental results validate the effectiveness of the proposed
graph regression and show that the proposed GR-GCN achieves the
state-of-the-art performance on the widely used NTU RGB+D, UT-Kinect and SYSU
3D datasets.
Efficient Semantic Segmentation for Visual Bird's-eye View Interpretation
Authors: Timo Sämann, Karl Amende, Stefan Milz, Christian Witt, Martin Simon, Johannes Petzold
Date: 2018-11-29 00:00:00
Tasks: Segmentation, Semantic Segmentation

The ability to perform semantic segmentation in real-time capable
applications with limited hardware is of great importance. One such application
is the interpretation of the visual bird's-eye view, which requires the
semantic segmentation of the four omnidirectional camera images. In this paper,
we present an efficient semantic segmentation that sets new standards in terms
of runtime and hardware requirements. Our two main contributions are the
decrease of the runtime by parallelizing the ArgMax layer and the reduction of
hardware requirements by applying the channel pruning method to the ENet model.
Global Second-order Pooling Convolutional Networks
Authors: Zilin Gao, Jiangtao Xie, Qilong Wang, Peihua Li
Date: 2018-11-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object Recognition

Deep Convolutional Networks (ConvNets) are fundamental to, besides
large-scale visual recognition, a lot of vision tasks. As the primary goal of
the ConvNets is to characterize complex boundaries of thousands of classes in a
high-dimensional space, it is critical to learn higher-order representations
for enhancing non-linear modeling capability. Recently, Global Second-order
Pooling (GSoP), plugged at the end of networks, has attracted increasing
attentions, achieving much better performance than classical, first-order
networks in a variety of vision tasks. However, how to effectively introduce
higher-order representation in earlier layers for improving non-linear
capability of ConvNets is still an open problem. In this paper, we propose a
novel network model introducing GSoP across from lower to higher layers for
exploiting holistic image information throughout a network. Given an input 3D
tensor outputted by some previous convolutional layer, we perform GSoP to
obtain a covariance matrix which, after nonlinear transformation, is used for
tensor scaling along channel dimension. Similarly, we can perform GSoP along
spatial dimension for tensor scaling as well. In this way, we can make full use
of the second-order statistics of the holistic image throughout a network. The
proposed networks are thoroughly evaluated on large-scale ImageNet-1K, and
experiments have shown that they outperformed non-trivially the counterparts
while achieving state-of-the-art results.
Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose
Authors: Daniil Osokin
Date: 2018-11-29 00:00:00
Tasks: CPU, Multi-Person Pose Estimation, Pose Estimation

In this work we adapt multi-person pose estimation architecture to use it on
edge devices. We follow the bottom-up approach from OpenPose, the winner of
COCO 2016 Keypoints Challenge, because of its decent quality and robustness to
number of people inside the frame. With proposed network design and optimized
post-processing code the full solution runs at 28 frames per second (fps) on
Intel$\unicode{xAE}$ NUC 6i7KYB mini PC and 26 fps on Core$^{TM}$ i7-6850K CPU.
The network model has 4.1M parameters and 9 billions floating-point operations
(GFLOPs) complexity, which is just ~15% of the baseline 2-stage OpenPose with
almost the same quality. The code and model are available as a part of
Intel$\unicode{xAE}$ OpenVINO$^{TM}$ Toolkit.