pwc-archive/papers-with-abstracts huggingface dataset:
SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization
Authors: Hoi-To Wai, Nikolaos M. Freris, Angelia Nedic, Anna Scaglione
Date: 2018-03-22 00:00:00
Tasks: Distributed Optimization

We propose and analyze a new stochastic gradient method, which we call
Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum
optimization problems. SUCAG constitutes an unbiased total gradient tracking
technique that uses Hessian information to accelerate con- vergence. We analyze
our method under the general asynchronous model of computation, in which each
function is selected infinitely often with possibly unbounded (but sublinear)
delay. For strongly convex problems, we establish linear convergence for the
SUCAG method. When the initialization point is sufficiently close to the
optimal solution, the established convergence rate is only dependent on the
condition number of the problem, making it strictly faster than the known rate
for the SAGA method. Furthermore, we describe a Markov-driven approach of
implementing the SUCAG method in a distributed asynchronous multi-agent
setting, via gossiping along a random walk on an undirected communication
graph. We show that our analysis applies as long as the graph is connected and,
notably, establishes an asymptotic linear convergence rate that is robust to
the graph topology. Numerical results demonstrate the merits of our algorithm
over existing methods.
Deep Pose Consensus Networks
Authors: Geonho Cha, Minsik Lee, Jungchan Cho, Songhwai Oh
Date: 2018-03-22 00:00:00

In this paper, we address the problem of estimating a 3D human pose from a single image, which is important but difficult to solve due to many reasons, such as self-occlusions, wild appearance changes, and inherent ambiguities of 3D estimation from a 2D cue. These difficulties make the problem ill-posed, which have become requiring increasingly complex estimators to enhance the performance. On the other hand, most existing methods try to handle this problem based on a single complex estimator, which might not be good solutions. In this paper, to resolve this issue, we propose a multiple-partial-hypothesis-based framework for the problem of estimating 3D human pose from a single image, which can be fine-tuned in an end-to-end fashion. We first select several joint groups from a human joint model using the proposed sampling scheme, and estimate the 3D poses of each joint group separately based on deep neural networks. After that, they are aggregated to obtain the final 3D poses using the proposed robust optimization formula. The overall procedure can be fine-tuned in an end-to-end fashion, resulting in better performance. In the experiments, the proposed framework shows the state-of-the-art performances on popular benchmark data sets, namely Human3.6M and HumanEva, which demonstrate the effectiveness of the proposed framework.
Beyond Low-Rank Representations: Orthogonal Clustering Basis Reconstruction with Optimized Graph Structure for Multi-view Spectral Clustering
Authors: Yang Wang, Lin Wu
Date: 2017-08-04 00:00:00
Tasks: Clustering

Low-Rank Representation (LRR) is arguably one of the most powerful paradigms
for Multi-view spectral clustering, which elegantly encodes the multi-view
local graph/manifold structures into an intrinsic low-rank self-expressive data
similarity embedded in high-dimensional space, to yield a better graph
partition than their single-view counterparts. In this paper we revisit it with
a fundamentally different perspective by discovering LRR as essentially a
latent clustered orthogonal projection based representation winged with an
optimized local graph structure for spectral clustering; each column of the
representation is fundamentally a cluster basis orthogonal to others to
indicate its members, which intuitively projects the view-specific feature
representation to be the one spanned by all orthogonal basis to characterize
the cluster structures. Upon this finding, we propose our technique with the
followings: (1) We decompose LRR into latent clustered orthogonal
representation via low-rank matrix factorization, to encode the more flexible
cluster structures than LRR over primal data objects; (2) We convert the
problem of LRR into that of simultaneously learning orthogonal clustered
representation and optimized local graph structure for each view; (3) The
learned orthogonal clustered representations and local graph structures enjoy
the same magnitude for multi-view, so that the ideal multi-view consensus can
be readily achieved. The experiments over multi-view datasets validate its
superiority.
A Dataset To Evaluate The Representations Learned By Video Prediction Models
Authors: Ryan Szeto, Simon Stent, German Ros, Jason J. Corso
Date: 2018-02-25 00:00:00
Tasks: Video Prediction

We present a parameterized synthetic dataset called Moving Symbols to support
the objective study of video prediction networks. Using several instantiations
of the dataset in which variation is explicitly controlled, we highlight issues
in an existing state-of-the-art approach and propose the use of a performance
metric with greater semantic meaning to improve experimental interpretability.
Our dataset provides canonical test cases that will help the community better
understand, and eventually improve, the representations learned by such
networks in the future. Code is available at
https://github.com/rszeto/moving-symbols .
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks
Authors: Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung
Date: 2018-03-22 00:00:00
Tasks: Generative Adversarial Network, Time Series Analysis

We suggest ways to enforce given constraints in the output of a Generative Adversarial Network (GAN) generator both for interpolation and extrapolation (prediction). For the case of dynamical systems, given a time series, we wish to train GAN generators that can be used to predict trajectories starting from a given initial condition. In this setting, the constraints can be in algebraic and/or differential form. Even though we are predominantly interested in the case of extrapolation, we will see that the tasks of interpolation and extrapolation are related. However, they need to be treated differently. For the case of interpolation, the incorporation of constraints is built into the training of the GAN. The incorporation of the constraints respects the primary game-theoretic setup of a GAN so it can be combined with existing algorithms. However, it can exacerbate the problem of instability during training that is well-known for GANs. We suggest adding small noise to the constraints as a simple remedy that has performed well in our numerical experiments. The case of extrapolation (prediction) is more involved. During training, the GAN generator learns to interpolate a noisy version of the data and we enforce the constraints. This approach has connections with model reduction that we can utilize to improve the efficiency and accuracy of the training. Depending on the form of the constraints, we may enforce them also during prediction through a projection step. We provide examples of linear and nonlinear systems of differential equations to illustrate the various constructions.
CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
Authors: Ganesh Iyer, R. Karnik Ram., J. Krishna Murthy, K. Madhava Krishna
Date: 2018-03-22 00:00:00
Tasks: Camera Calibration, Domain Adaptation

3D LiDARs and 2D cameras are increasingly being used alongside each other in sensor rigs for perception tasks. Before these sensors can be used to gather meaningful data, however, their extrinsics (and intrinsics) need to be accurately calibrated, as the performance of the sensor rig is extremely sensitive to these calibration parameters. A vast majority of existing calibration techniques require significant amounts of data and/or calibration targets and human effort, severely impacting their applicability in large-scale production systems. We address this gap with CalibNet: a self-supervised deep network capable of automatically estimating the 6-DoF rigid body transformation between a 3D LiDAR and a 2D camera in real-time. CalibNet alleviates the need for calibration targets, thereby resulting in significant savings in calibration efforts. During training, the network only takes as input a LiDAR point cloud, the corresponding monocular image, and the camera calibration matrix K. At train time, we do not impose direct supervision (i.e., we do not directly regress to the calibration parameters, for example). Instead, we train the network to predict calibration parameters that maximize the geometric and photometric consistency of the input images and point clouds. CalibNet learns to iteratively solve the underlying geometric problem and accurately predicts extrinsic calibration parameters for a wide range of mis-calibrations, without requiring retraining or domain adaptation. The project page is hosted at https://epiception.github.io/CalibNet
Data Augmentation Generative Adversarial Networks
Authors: Antreas Antoniou, Amos Storkey, Harrison Edwards
Date: 2017-11-12 00:00:00
Proceeding: ICLR 2018 1
Tasks: Data Augmentation, Few-Shot Learning, Generative Adversarial Network

Effective training of neural networks requires much data. In the low-data
regime, parameters are underdetermined, and learnt networks generalise poorly.
Data Augmentation alleviates this by using existing data more effectively.
However standard data augmentation produces only limited plausible alternative
data. Given there is potential to generate a much broader set of augmentations,
we design and train a generative model to do data augmentation. The model,
based on image conditional Generative Adversarial Networks, takes data from a
source domain and learns to take any data item and generalise it to generate
other within-class data items. As this generative process does not depend on
the classes themselves, it can be applied to novel unseen classes of data. We
show that a Data Augmentation Generative Adversarial Network (DAGAN) augments
standard vanilla classifiers well. We also show a DAGAN can enhance few-shot
learning systems such as Matching Networks. We demonstrate these approaches on
Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and VGG-Face data. In
our experiments we can see over 13% increase in accuracy in the low-data regime
experiments in Omniglot (from 69% to 82%), EMNIST (73.9% to 76%) and VGG-Face
(4.5% to 12%); in Matching Networks for Omniglot we observe an increase of 0.5%
(from 96.9% to 97.4%) and an increase of 1.8% in EMNIST (from 59.5% to 61.3%).
Comparing Fixed and Adaptive Computation Time for Recurrent Neural Networks
Authors: Daniel Fojo, Víctor Campos, Xavier Giro-i-Nieto
Date: 2018-03-21 00:00:00

Adaptive Computation Time for Recurrent Neural Networks (ACT) is one of the
most promising architectures for variable computation. ACT adapts to the input
sequence by being able to look at each sample more than once, and learn how
many times it should do it. In this paper, we compare ACT to Repeat-RNN, a
novel architecture based on repeating each sample a fixed number of times. We
found surprising results, where Repeat-RNN performs as good as ACT in the
selected tasks. Source code in TensorFlow and PyTorch is publicly available at
https://imatge-upc.github.io/danifojo-2018-repeatrnn/
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
Authors: Yifan Ding, Liqiang Wang, Deliang Fan, Boqing Gong
Date: 2018-02-08 00:00:00
Tasks: Vocal Bursts Valence Prediction

The recent success of deep neural networks is powered in part by large-scale
well-labeled training data. However, it is a daunting task to laboriously
annotate an ImageNet-like dateset. On the contrary, it is fairly convenient,
fast, and cheap to collect training images from the Web along with their noisy
labels. This signifies the need of alternative approaches to training deep
neural networks using such noisy labels. Existing methods tackling this problem
either try to identify and correct the wrong labels or reweigh the data terms
in the loss function according to the inferred noisy rates. Both strategies
inevitably incur errors for some of the data points. In this paper, we contend
that it is actually better to ignore the labels of some of the data points than
to keep them if the labels are incorrect, especially when the noisy rate is
high. After all, the wrong labels could mislead a neural network to a bad local
optimum. We suggest a two-stage framework for the learning from noisy labels.
In the first stage, we identify a small portion of images from the noisy
training set of which the labels are correct with a high probability. The noisy
labels of the other images are ignored. In the second stage, we train a deep
neural network in a semi-supervised manner. This framework effectively takes
advantage of the whole training set and yet only a portion of its labels that
are most likely correct. Experiments on three datasets verify the effectiveness
of our approach especially when the noisy rate is high.
Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization
Authors: Linchen Xiao, Arash Behboodi, Rudolf Mathar
Date: 2018-03-21 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation, Deep Learning, Transfer Learning

Considered as a data-driven approach, Fingerprinting Localization Solutions
(FPSs) enjoy huge popularity due to their good performance and minimal
environment information requirement. This papers addresses applications of
artificial intelligence to solve two problems in Received Signal Strength
Indicator (RSSI) based FPS, first the cumbersome training database construction
and second the extrapolation of fingerprinting algorithm for similar buildings
with slight environmental changes. After a concise overview of deep learning
design techniques, two main techniques widely used in deep learning are
exploited for the above mentioned issues namely data augmentation and transfer
learning. We train a multi-layer neural network that learns the mapping from
the observations to the locations. A data augmentation method is proposed to
increase the training database size based on the structure of RSSI measurements
and hence reducing effectively the amount of training data. Then it is shown
experimentally how a model trained for a particular building can be transferred
to a similar one by fine tuning with significantly smaller training numbers.
The paper implicitly discusses the new guidelines to consider about deep
learning designs when they are employed in a new application context.
Generative Image Inpainting with Contextual Attention
Authors: Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, Thomas S. Huang
Date: 2018-01-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Inpainting

Recent deep learning based approaches have shown promising results for the
challenging task of inpainting large missing regions in an image. These methods
can generate visually plausible image structures and textures, but often create
distorted structures or blurry textures inconsistent with surrounding areas.
This is mainly due to ineffectiveness of convolutional neural networks in
explicitly borrowing or copying information from distant spatial locations. On
the other hand, traditional texture and patch synthesis approaches are
particularly suitable when it needs to borrow textures from the surrounding
regions. Motivated by these observations, we propose a new deep generative
model-based approach which can not only synthesize novel image structures but
also explicitly utilize surrounding image features as references during network
training to make better predictions. The model is a feed-forward, fully
convolutional neural network which can process images with multiple holes at
arbitrary locations and with variable sizes during the test time. Experiments
on multiple datasets including faces (CelebA, CelebA-HQ), textures (DTD) and
natural images (ImageNet, Places2) demonstrate that our proposed approach
generates higher-quality inpainting results than existing ones. Code, demo and
models are available at: https://github.com/JiahuiYu/generative_inpainting.
FaceVR: Real-Time Facial Reenactment and Eye Gaze Control in Virtual Reality
Authors: Justus Thies, Michael Zollhöfer, Marc Stamminger, Christian Theobalt, Matthias Nießner
Date: 2016-10-11 00:00:00

We propose FaceVR, a novel image-based method that enables video
teleconferencing in VR based on self-reenactment. State-of-the-art face
tracking methods in the VR context are focused on the animation of rigged 3d
avatars. While they achieve good tracking performance the results look
cartoonish and not real. In contrast to these model-based approaches, FaceVR
enables VR teleconferencing using an image-based technique that results in
nearly photo-realistic outputs. The key component of FaceVR is a robust
algorithm to perform real-time facial motion capture of an actor who is wearing
a head-mounted display (HMD), as well as a new data-driven approach for eye
tracking from monocular videos. Based on reenactment of a prerecorded stereo
video of the person without the HMD, FaceVR incorporates photo-realistic
re-rendering in real time, thus allowing artificial modifications of face and
eye appearances. For instance, we can alter facial expressions or change gaze
directions in the prerecorded target video. In a live setup, we apply these
newly-introduced algorithmic components.
Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery
Authors: Yichen Wu, Yair Rivenson, Yibo Zhang, Zhensong Wei, Harun Gunaydin, Xing Lin, Aydogan Ozcan
Date: 2018-03-21 00:00:00
Tasks: Image Reconstruction

Holography encodes the three dimensional (3D) information of a sample in the
form of an intensity-only recording. However, to decode the original sample
image from its hologram(s), auto-focusing and phase-recovery are needed, which
are in general cumbersome and time-consuming to digitally perform. Here we
demonstrate a convolutional neural network (CNN) based approach that
simultaneously performs auto-focusing and phase-recovery to significantly
extend the depth-of-field (DOF) in holographic image reconstruction. For this,
a CNN is trained by using pairs of randomly de-focused back-propagated
holograms and their corresponding in-focus phase-recovered images. After this
training phase, the CNN takes a single back-propagated hologram of a 3D sample
as input to rapidly achieve phase-recovery and reconstruct an in focus image of
the sample over a significantly extended DOF. This deep learning based DOF
extension method is non-iterative, and significantly improves the algorithm
time-complexity of holographic image reconstruction from O(nm) to O(1), where n
refers to the number of individual object points or particles within the sample
volume, and m represents the focusing search space within which each object
point or particle needs to be individually focused. These results highlight
some of the unique opportunities created by data-enabled statistical image
reconstruction methods powered by machine learning, and we believe that the
presented approach can be broadly applicable to computationally extend the DOF
of other imaging modalities.
Robust Blind Deconvolution via Mirror Descent
Authors: Sathya N. Ravi, Ronak Mehta, Vikas Singh
Date: 2018-03-21 00:00:00

We revisit the Blind Deconvolution problem with a focus on understanding its
robustness and convergence properties. Provable robustness to noise and other
perturbations is receiving recent interest in vision, from obtaining immunity
to adversarial attacks to assessing and describing failure modes of algorithms
in mission critical applications. Further, many blind deconvolution methods
based on deep architectures internally make use of or optimize the basic
formulation, so a clearer understanding of how this sub-module behaves, when it
can be solved, and what noise injection it can tolerate is a first order
requirement. We derive new insights into the theoretical underpinnings of blind
deconvolution. The algorithm that emerges has nice convergence guarantees and
is provably robust in a sense we formalize in the paper. Interestingly, these
technical results play out very well in practice, where on standard datasets
our algorithm yields results competitive with or superior to the state of the
art. Keywords: blind deconvolution, robust continuous optimization
Task dependent Deep LDA pruning of neural networks
Authors: Qing Tian, Tal Arbel, James J. Clark
Date: 2018-03-21 00:00:00

With deep learning's success, a limited number of popular deep nets have been widely adopted for various vision tasks. However, this usually results in unnecessarily high complexities and possibly many features of low task utility. In this paper, we address this problem by introducing a task-dependent deep pruning framework based on Fisher's Linear Discriminant Analysis (LDA). The approach can be applied to convolutional, fully-connected, and module-based deep network structures, in all cases leveraging the high decorrelation of neuron motifs found in the pre-decision space and cross-layer deconv dependency. Moreover, we examine our approach's potential in network architecture search for specific tasks and analyze the influence of our pruning on model robustness to noises and adversarial attacks. Experimental results on datasets of generic objects (ImageNet, CIFAR100) as well as domain specific tasks (Adience, and LFWA) illustrate our framework's superior performance over state-of-the-art pruning approaches and fixed compact nets (e.g. SqueezeNet, MobileNet). The proposed method successfully maintains comparable accuracies even after discarding most parameters (98%-99% for VGG16, up to 82% for the already compact InceptionNet) and with significant FLOP reductions (83% for VGG16, up to 64% for InceptionNet). Through pruning, we can also derive smaller, but more accurate and more robust models suitable for the task.
Ricci Curvature and the Manifold Learning Problem
Authors: Antonio G. Ache, Micah W. Warren
Date: 2014-10-13 00:00:00

Consider a sample of $n$ points taken i.i.d from a submanifold $\Sigma$ of
Euclidean space. We show that there is a way to estimate the Ricci curvature of
$\Sigma$ with respect to the induced metric from the sample. Our method is
grounded in the notions of Carr\'e du Champ for diffusion semi-groups, the
theory of Empirical processes and local Principal Component Analysis.
Seglearn: A Python Package for Learning Sequences and Time Series
Authors: David M. Burns, Cari M. Whyne
Date: 2018-03-21 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, regression, Time Series, Time Series Analysis

Seglearn is an open-source python package for machine learning time series or
sequences using a sliding window segmentation approach. The implementation
provides a flexible pipeline for tackling classification, regression, and
forecasting problems with multivariate sequence and contextual data. This
package is compatible with scikit-learn and is listed under scikit-learn
Related Projects. The package depends on numpy, scipy, and scikit-learn.
Seglearn is distributed under the BSD 3-Clause License. Documentation includes
a detailed API description, user guide, and examples. Unit tests provide a high
degree of code coverage.