pwc-archive/papers-with-abstracts huggingface dataset:
Deep Curiosity Loops in Social Environments
Authors: Jonatan Barkan, Goren Gordon
Date: 2018-06-10 00:00:00
Tasks: Hand Detection, Optical Flow Estimation, Reinforcement Learning, Reinforcement Learning (RL)

Inspired by infants' intrinsic motivation to learn, which values informative
sensory channels contingent on their immediate social environment, we developed
a deep curiosity loop (DCL) architecture. The DCL is composed of a learner,
which attempts to learn a forward model of the agent's state-action transition,
and a novel reinforcement-learning (RL) component, namely, an
Action-Convolution Deep Q-Network, which uses the learner's prediction error as
reward. The environment for our agent is composed of visual social scenes,
composed of sitcom video streams, thereby both the learner and the RL are
constructed as deep convolutional neural networks. The agent's learner learns
to predict the zero-th order of the dynamics of visual scenes, resulting in
intrinsic rewards proportional to changes within its social environment. The
sources of these socially informative changes within the sitcom are
predominantly motions of faces and hands, leading to the unsupervised
curiosity-based learning of social interaction features. The face and hand
detection is represented by the value function and the social interaction
optical-flow is represented by the policy. Our results suggest that face and
hand detection are emergent properties of curiosity-based learning embedded in
social environments.
Transformationally Identical and Invariant Convolutional Neural Networks through Symmetric Element Operators
Authors: Shih Chung B. Lo, Matthew T. Freedman, Seong K. Mun, Shuo Gu
Date: 2018-06-10 00:00:00
Tasks: Data Augmentation

Mathematically speaking, a transformationally invariant operator, such as a
transformationally identical (TI) matrix kernel (i.e., K= T{K}), commutes with
the transformation (T{.}) itself when they operate on the first operand matrix.
We found that by consistently applying the same type of TI kernels in a
convolutional neural networks (CNN) system, the commutative property holds
throughout all layers of convolution processes with and without involving an
activation function and/or a 1D convolution across channels within a layer. We
further found that any CNN possessing the same TI kernel property for all
convolution layers followed by a flatten layer with weight sharing among their
transformation corresponding elements would output the same result for all
transformation versions of the original input vector. In short, CNN[ Vi ] =
CNN[ T{Vi} ] providing every K = T{K} in CNN, where Vi denotes input vector and
CNN[.] represents the whole CNN process as a function of input vector that
produces an output vector. With such a transformationally identical CNN
(TI-CNN) system, each transformation, that is not associated with a predefined
TI used in data augmentation, would inherently include all of its corresponding
transformation versions of the input vector for the training. Hence the use of
same TI property for every kernel in the CNN would serve as an orientation or a
translation independent training guide in conjunction with the
error-backpropagation during the training. This TI kernel property is desirable
for applications requiring a highly consistent output result from corresponding
transformation versions of an input. Several C programming routines are
provided to facilitate interested parties of using the TI-CNN technique which
is expected to produce a better generalization performance than its ordinary
CNN counterpart.
Segmentation of Instances by Hashing
Authors: J. D. Curtó, I. C. Zarza, A. Smola, L. Van Gool
Date: 2017-02-27 00:00:00
Tasks: Segmentation

We propose a novel approach to address the Simultaneous Detection and
Segmentation problem. Using hierarchical structures we use an efficient and
accurate procedure that exploits the hierarchy feature information using
Locality Sensitive Hashing. We build on recent work that utilizes convolutional
neural networks to detect bounding boxes in an image and then use the top
similar hierarchical region that best fits each bounding box after hashing, we
call this approach CZ Segmentation. We then refine our final segmentation
results by automatic hierarchy pruning. CZ Segmentation introduces a train-free
alternative to Hypercolumns. We conduct extensive experiments on PASCAL VOC
2012 segmentation dataset, showing that CZ gives competitive state-of-the-art
object segmentations.
McKernel: A Library for Approximate Kernel Expansions in Log-linear Time
Authors: Joachim D. Curtó, Irene C. Zarza, Feng Yang, Alexander J. Smola, Fernando de la Torre, Chong-Wah Ngo, Luc van Gool
Date: 2017-02-27 00:00:00
Tasks: CPU, General Classification

Kernel Methods Next Generation (KMNG) introduces a framework to use kernel
approximates in the mini-batch setting with SGD Optimizer as an alternative to
Deep Learning. McKernel is a C++ library for KMNG ML Large-scale. It contains a
CPU optimized implementation of the Fastfood algorithm that allows the
computation of approximated kernel expansions in log-linear time. The algorithm
requires to compute the product of Walsh Hadamard Transform (WHT) matrices. A
cache friendly SIMD Fast Walsh Hadamard Transform (FWHT) that achieves
compelling speed and outperforms current state-of-the-art methods has been
developed. McKernel allows to obtain non-linear classification combining
Fastfood and a linear classifier.
Enhancing Convolutional Neural Networks for Face Recognition with Occlusion Maps and Batch Triplet Loss
Authors: Daniel Sáez Trigueros, Li Meng, Margaret Hartnett
Date: 2017-07-25 00:00:00
Tasks: Face Recognition, Triplet

Despite the recent success of convolutional neural networks for computer
vision applications, unconstrained face recognition remains a challenge. In
this work, we make two contributions to the field. Firstly, we consider the
problem of face recognition with partial occlusions and show how current
approaches might suffer significant performance degradation when dealing with
this kind of face images. We propose a simple method to find out which parts of
the human face are more important to achieve a high recognition rate, and use
that information during training to force a convolutional neural network to
learn discriminative features from all the face regions more equally, including
those that typical approaches tend to pay less attention to. We test the
accuracy of the proposed method when dealing with real-life occlusions using
the AR face database. Secondly, we propose a novel loss function called batch
triplet loss that improves the performance of the triplet loss by adding an
extra term to the loss function to cause minimisation of the standard deviation
of both positive and negative scores. We show consistent improvement in the
Labeled Faces in the Wild (LFW) benchmark by applying both proposed adjustments
to the convolutional neural network training.
VoxelAtlasGAN: 3D Left Ventricle Segmentation on Echocardiography with Atlas Guided Generation and Voxel-to-voxel Discrimination
Authors: Suyu Dong, Gongning Luo, Kuanquan Wang, Shaodong Cao, Ashley Mercado, Olga Shmuilovich, Henggui Zhang, Shuo Li
Date: 2018-06-10 00:00:00
Tasks: Left Ventricle Segmentation, LV Segmentation, Segmentation, Self-Learning

3D left ventricle (LV) segmentation on echocardiography is very important for
diagnosis and treatment of cardiac disease. It is not only because of that
echocardiography is a real-time imaging technology and widespread in clinical
application, but also because of that LV segmentation on 3D echocardiography
can provide more full volume information of heart than LV segmentation on 2D
echocardiography. However, 3D LV segmentation on echocardiography is still an
open and challenging task owing to the lower contrast, higher noise and data
dimensionality, limited annotation of 3D echocardiography. In this paper, we
proposed a novel real-time framework, i.e., VoxelAtlasGAN, for 3D LV
segmentation on 3D echocardiography. This framework has three contributions: 1)
It is based on voxel-to-voxel conditional generative adversarial nets (cGAN).
For the first time, cGAN is used for 3D LV segmentation on echocardiography.
And cGAN advantageously fuses substantial 3D spatial context information from
3D echocardiography by self-learning structured loss; 2) For the first time, it
embeds the atlas into an end-to-end optimization framework, which uses 3D LV
atlas as a powerful prior knowledge to improve the inference speed, address the
lower contrast and the limited annotation problems of 3D echocardiography; 3)
It combines traditional discrimination loss and the new proposed consistent
constraint, which further improves the generalization of the proposed
framework. VoxelAtlasGAN was validated on 60 subjects on 3D echocardiography
and it achieved satisfactory segmentation results and high inference speed. The
mean surface distance is 1.85 mm, the mean hausdorff surface distance is 7.26
mm, mean dice is 0.953, the correlation of EF is 0.918, and the mean inference
speed is 0.1s. These results have demonstrated that our proposed method has
great potential for clinical application
Capacity Releasing Diffusion for Speed and Locality
Authors: Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao
Date: 2017-06-19 00:00:00
Tasks: Clustering, Graph Clustering

Diffusions and related random walk procedures are of central importance in
many areas of machine learning, data analysis, and applied mathematics. Because
they spread mass agnostically at each step in an iterative manner, they can
sometimes spread mass "too aggressively," thereby failing to find the "right"
clusters. We introduce a novel Capacity Releasing Diffusion (CRD) Process,
which is both faster and stays more local than the classical spectral diffusion
process. As an application, we use our CRD Process to develop an improved local
algorithm for graph clustering. Our local graph clustering method can find
local clusters in a model of clustering where one begins the CRD Process in a
cluster whose vertices are connected better internally than externally by an
$O(\log^2 n)$ factor, where $n$ is the number of nodes in the cluster. Thus,
our CRD Process is the first local graph clustering algorithm that is not
subject to the well-known quadratic Cheeger barrier. Our result requires a
certain smoothness condition, which we expect to be an artifact of our
analysis. Our empirical evaluation demonstrates improved results, in particular
for realistic social graphs where there are moderately good---but not very
good---clusters.
Implicit Policy for Reinforcement Learning
Authors: Yunhao Tang, Shipra Agrawal
Date: 2018-06-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce Implicit Policy, a general class of expressive policies that can
flexibly represent complex action distributions in reinforcement learning, with
efficient algorithms to compute entropy regularized policy gradients. We
empirically show that, despite its simplicity in implementation, entropy
regularization combined with a rich policy class can attain desirable
properties displayed under maximum entropy reinforcement learning framework,
such as robustness and multi-modality.
k-Space Deep Learning for Reference-free EPI Ghost Correction
Authors: Juyoung Lee, Yoseob Han, Jae-Kyun Ryu, Jang-Yeon Park, Jong Chul Ye
Date: 2018-06-01 00:00:00
Tasks: Deep Learning, Matrix Completion

Nyquist ghost artifacts in EPI are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field MRI due to the non-linear and time-varying local magnetic field changes. Recently, it was shown that the problem of ghost correction can be reformulated as k-space interpolation problem that can be solved using structured low-rank Hankel matrix approaches. Another recent work showed that data driven Hankel matrix decomposition can be reformulated to exhibit similar structures as deep convolutional neural network. By synergistically combining these findings, we propose a k-space deep learning approach that immediately corrects the phase mismatch without a reference scan in both accelerated and non-accelerated EPI acquisitions. To take advantage of the even and odd-phase directional redundancy, the k-space data is divided into two channels configured with even and odd phase encodings. The redundancies between coils are also exploited by stacking the multi-coil k-space data into additional input channels. Then, our k-space ghost correction network is trained to learn the interpolation kernel to estimate the missing virtual k-space data. For the accelerated EPI data, the same neural network is trained to directly estimate the interpolation kernels for missing k-space data from both ghost and subsampling. Reconstruction results using 3T and 7T in-vivo data showed that the proposed method outperformed the image quality compared to the existing methods, and the computing time is much faster.The proposed k-space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high-field MRI without changing the current acquisition protocol.
Weighted Tanimoto Coefficient for 3D Molecule Structure Similarity Measurement
Authors: Siti Asmah Bero, Azah Kamilah Muda, Yun-Huoy Choo, Noor Azilah Muda, Satrya Fajri Pratama
Date: 2018-06-10 00:00:00
Tasks: Drug Discovery

Similarity searching of molecular structure has been an important application
in the Chemoinformatics, especially in drug discovery. Similarity searching is
a common method used for identification of molecular structure. It involve
three main principal component of similarity searching: structure
representation; weighting scheme; and similarity coefficient. In this paper, we
introduces Weighted Tanimoto Coefficient based on weighted Euclidean distance
in order to investigate the effect of weight function on the result for
similarity searching. The Tanimoto coefficient is one of the popular similarity
coefficients used to measure the similarity between pairs of the molecule. The
most of research area found that the similarity searching is based on binary or
fingerprint data. Meanwhile, we used non-binary data and was set amphetamine
structure as a reference or targeted structure and the rest of the dataset
becomes a database structure. Throughout this study, it showed that there is
definitely gives a different result between a similarity searching with and
without weight.
k-Space Deep Learning for Parallel MRI: Application to Time-Resolved MR Angiography
Authors: Eunju Cha, Eung Yeop Kim, Jong Chul Ye
Date: 2018-06-03 00:00:00

Time-resolved angiography with interleaved stochastic trajectories (TWIST)
has been widely used for dynamic contrast enhanced MRI (DCE-MRI). To achieve
highly accelerated acquisitions, TWIST combines the periphery of the k-space
data from several adjacent frames to reconstruct one temporal frame. However,
this view-sharing scheme limits the true temporal resolution of TWIST.
Moreover, the k-space sampling patterns have been specially designed for a
specific generalized autocalibrating partial parallel acquisition (GRAPPA)
factor so that it is not possible to reduce the number of view-sharing once the
k-data is acquired. To address these issues, this paper proposes a novel
k-space deep learning approach for parallel MRI. In particular, we have
designed our neural network so that accurate k-space interpolations are
performed simultaneously for multiple coils by exploiting the redundancies
along the coils and images. Reconstruction results using in vivo TWIST data set
confirm that the proposed method can immediately generate high-quality
reconstruction results with various choices of view- sharing, allowing us to
exploit the trade-off between spatial and temporal resolution in time-resolved
MR angiography.
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups
Authors: Risi Kondor, Shubhendu Trivedi
Date: 2018-02-11 00:00:00
Proceeding: ICML 2018 7

Convolutional neural networks have been extremely successful in the image
recognition domain because they ensure equivariance to translations. There have
been many recent attempts to generalize this framework to other domains,
including graphs and data lying on manifolds. In this paper we give a rigorous,
theoretical treatment of convolution and equivariance in neural networks with
respect to not just translations, but the action of any compact group. Our main
result is to prove that (given some natural constraints) convolutional
structure is not just a sufficient, but also a necessary condition for
equivariance to the action of a compact group. Our exposition makes use of
concepts from representation theory and noncommutative harmonic analysis and
derives new generalized convolution formulae.
Cross-Lingual Task-Specific Representation Learning for Text Classification in Resource Poor Languages
Authors: Nurendra Choudhary, Rajat Singh, Manish Shrivastava
Date: 2018-06-10 00:00:00
Tasks: Classification, General Classification, Representation Learning, Sentiment Analysis, text-classification, Text Classification

Neural network models have shown promising results for text classification.
However, these solutions are limited by their dependence on the availability of
annotated data.
  The prospect of leveraging resource-rich languages to enhance the text
classification of resource-poor languages is fascinating. The performance on
resource-poor languages can significantly improve if the resource availability
constraints can be offset. To this end, we present a twin Bidirectional Long
Short Term Memory (Bi-LSTM) network with shared parameters consolidated by a
contrastive loss function (based on a similarity metric). The model learns the
representation of resource-poor and resource-rich sentences in a common space
by using the similarity between their assigned annotation tags. Hence, the
model projects sentences with similar tags closer and those with different tags
farther from each other. We evaluated our model on the classification tasks of
sentiment analysis and emoji prediction for resource-poor languages - Hindi and
Telugu and resource-rich languages - English and Spanish. Our model
significantly outperforms the state-of-the-art approaches in both the tasks
across all metrics.
Free-Form Image Inpainting with Gated Convolution
Authors: Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, Thomas Huang
Date: 2018-06-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: feature selection, Form, Image Inpainting, valid

We present a generative image inpainting system to complete images with free-form mask and guidance. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers. Moreover, as free-form masks may appear anywhere in images with any shape, global and local GANs designed for a single rectangular mask are not applicable. Thus, we also present a patch-based GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminator on dense image patches. SN-PatchGAN is simple in formulation, fast and stable in training. Results on automatic image inpainting and user-guided extension demonstrate that our system generates higher-quality and more flexible results than previous methods. Our system helps user quickly remove distracting objects, modify image layouts, clear watermarks and edit faces. Code, demo and models are available at: https://github.com/JiahuiYu/generative_inpainting
A Simplified Active Calibration algorithm for Focal Length Estimation
Authors: Mehdi Faraji, Anup Basu
Date: 2018-06-10 00:00:00

We introduce new linear mathematical formulations to calculate the focal
length of a camera in an active platform. Through mathematical derivations, we
show that the focal lengths in each direction can be estimated using only one
point correspondence that relates images taken before and after a degenerate
rotation of the camera. The new formulations will be beneficial in robotic and
dynamic surveillance environments when the camera needs to be calibrated while
it freely moves and zooms. By establishing a correspondence between only two
images taken after slightly panning and tilting the camera and a reference
image, our proposed Simplified Calibration Method is able to calculate the
focal length of the camera. We extensively evaluate the derived formulations on
a simulated camera, 3D scenes and real-world images. Our error analysis over
simulated and real images indicates that the proposed Simplified Active
Calibration formulation estimates the parameters of a camera with low error
rates.
A Scalable Framework for Trajectory Prediction
Authors: Punit Rathore, Dheeraj Kumar, Sutharshan Rajasegarar, Marimuthu Palaniswami, James C. Bezdek
Date: 2018-06-10 00:00:00
Tasks: Clustering, Management, Prediction, Trajectory Clustering, Trajectory Prediction

Trajectory prediction (TP) is of great importance for a wide range of
location-based applications in intelligent transport systems such as
location-based advertising, route planning, traffic management, and early
warning systems. In the last few years, the widespread use of GPS navigation
systems and wireless communication technology enabled vehicles has resulted in
huge volumes of trajectory data. The task of utilizing this data employing
spatio-temporal techniques for trajectory prediction in an efficient and
accurate manner is an ongoing research problem. Existing TP approaches are
limited to short-term predictions. Moreover, they cannot handle a large volume
of trajectory data for long-term prediction. To address these limitations, we
propose a scalable clustering and Markov chain based hybrid framework, called
Traj-clusiVAT-based TP, for both short-term and long-term trajectory
prediction, which can handle a large number of overlapping trajectories in a
dense road network. Traj-clusiVAT can also determine the number of clusters,
which represent different movement behaviours in input trajectory data. In our
experiments, we compare our proposed approach with a mixed Markov model
(MMM)-based scheme, and a trajectory clustering, NETSCAN-based TP method for
both short- and long-term trajectory predictions. We performed our experiments
on two real, vehicle trajectory datasets, including a large-scale trajectory
dataset consisting of 3.28 million trajectories obtained from 15,061 taxis in
Singapore over a period of one month. Experimental results on two real
trajectory datasets show that our proposed approach outperforms the existing
approaches in terms of both short- and long-term prediction performances, based
on prediction accuracy and distance error (in km).