pwc-archive/papers-with-abstracts huggingface dataset:
Learning for Video Super-Resolution through HR Optical Flow Estimation
Authors: Longguang Wang, Yulan Guo, Zaiping Lin, Xinpu Deng, Wei An
Date: 2018-09-23 00:00:00
Tasks: Motion Compensation, Optical Flow Estimation, Super-Resolution, Video Super-Resolution

Video super-resolution (SR) aims to generate a sequence of high-resolution
(HR) frames with plausible and temporally consistent details from their
low-resolution (LR) counterparts. The generation of accurate correspondence
plays a significant role in video SR. It is demonstrated by traditional video
SR methods that simultaneous SR of both images and optical flows can provide
accurate correspondences and better SR results. However, LR optical flows are
used in existing deep learning based methods for correspondence generation. In
this paper, we propose an end-to-end trainable video SR framework to
super-resolve both images and optical flows. Specifically, we first propose an
optical flow reconstruction network (OFRnet) to infer HR optical flows in a
coarse-to-fine manner. Then, motion compensation is performed according to the
HR optical flows. Finally, compensated LR inputs are fed to a super-resolution
network (SRnet) to generate the SR results. Extensive experiments demonstrate
that HR optical flows provide more accurate correspondences than their LR
counterparts and improve both accuracy and consistency performance. Comparative
results on the Vid4 and DAVIS-10 datasets show that our framework achieves the
state-of-the-art performance.
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models
Authors: Shoubo Hu, Zhitang Chen, Vahid Partovi Nia, Laiwan Chan, Yanhui Geng
Date: 2018-09-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Causal Inference, Clustering

The inference of the causal relationship between a pair of observed variables
is a fundamental problem in science, and most existing approaches are based on
one single causal model. In practice, however, observations are often collected
from multiple sources with heterogeneous causal models due to certain
uncontrollable factors, which renders causal analysis results obtained by a
single model skeptical. In this paper, we generalize the Additive Noise Model
(ANM) to a mixture model, which consists of a finite number of ANMs, and
provide the condition of its causal identifiability. To conduct model
estimation, we propose Gaussian Process Partially Observable Model (GPPOM), and
incorporate independence enforcement into it to learn latent parameter
associated with each observation. Causal inference and clustering according to
the underlying generating mechanisms of the mixture model are addressed in this
work. Experiments on synthetic and real data demonstrate the effectiveness of
our proposed approach.
Identification and Visualization of the Underlying Independent Causes of the Diagnostic of Diabetic Retinopathy made by a Deep Learning Classifier
Authors: Jordi de la Torre, Aida Valls, Domenec Puig, Pere Romero-Aroca
Date: 2018-09-23 00:00:00
Tasks: Deep Learning, Diagnostic, General Classification, Medical Diagnosis

Interpretability is a key factor in the design of automatic classifiers for
medical diagnosis. Deep learning models have been proven to be a very effective
classification algorithm when trained in a supervised way with enough data. The
main concern is the difficulty of inferring rationale interpretations from
them. Different attempts have been done in last years in order to convert deep
learning classifiers from high confidence statistical black box machines into
self-explanatory models. In this paper we go forward into the generation of
explanations by identifying the independent causes that use a deep learning
model for classifying an image into a certain class. We use a combination of
Independent Component Analysis with a Score Visualization technique. In this
paper we study the medical problem of classifying an eye fundus image into 5
levels of Diabetic Retinopathy. We conclude that only 3 independent components
are enough for the differentiation and correct classification between the 5
disease standard classes. We propose a method for visualizing them and
detecting lesions from the generated visual maps.
A Kernel Embedding-based Approach for Nonstationary Causal Model Inference
Authors: Shoubo Hu, Zhitang Chen, Laiwan Chan
Date: 2018-09-23 00:00:00
Tasks: Causal Discovery

Although nonstationary data are more common in the real world, most existing
causal discovery methods do not take nonstationarity into consideration. In
this letter, we propose a kernel embedding-based approach, ENCI, for
nonstationary causal model inference where data are collected from multiple
domains with varying distributions. In ENCI, we transform the complicated
relation of a cause-effect pair into a linear model of variables of which
observations correspond to the kernel embeddings of the cause-and-effect
distributions in different domains. In this way, we are able to estimate the
causal direction by exploiting the causal asymmetry of the transformed linear
model. Furthermore, we extend ENCI to causal graph discovery for multiple
variables by transforming the relations among them into a linear nongaussian
acyclic model. We show that by exploiting the nonstationarity of distributions,
both cause-effect pairs and two kinds of causal graphs are identifiable under
mild conditions. Experiments on synthetic and real-world data are conducted to
justify the efficacy of ENCI over major existing methods.
Self Attention Grid for Person Re-Identification
Authors: Jean-Paul Ainam, Ke Qin, Guisong Liu
Date: 2018-09-23 00:00:00
Tasks: Person Re-Identification

In this paper, we present an attention mechanism scheme to improve person
re-identification task. Inspired by biology, we propose Self Attention Grid
(SAG) to discover the most informative parts from a high-resolution image using
its internal representation. In particular, given an input image, the proposed
model is fed with two copies of the same image and consists of two branches.
The upper branch processes the high-resolution image and learns high
dimensional feature representation while the lower branch processes the
low-resolution image and learn a filtering attention grid. We apply a max
filter operation to non-overlapping sub-regions on the high feature
representation before element-wise multiplied with the output of the second
branch. The feature maps of the second branch are subsequently weighted to
reflect the importance of each patch of the grid using a softmax operation. Our
attention module helps the network learn the most discriminative visual
features of multiple image regions and is specifically optimized to attend
feature representation at different levels. Extensive experiments on three
large-scale datasets show that our self-attention mechanism significantly
improves the baseline model and outperforms various state-of-art models by a
large margin.
A Learning Framework for High Precision Industrial Assembly
Authors: Yongxiang Fan, Jieliang Luo, Masayoshi Tomizuka
Date: 2018-09-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Vocal Bursts Intensity Prediction

Automatic assembly has broad applications in industries. Traditional assembly
tasks utilize predefined trajectories or tuned force control parameters, which
make the automatic assembly time-consuming, difficult to generalize, and not
robust to uncertainties. In this paper, we propose a learning framework for
high precision industrial assembly. The framework combines both the supervised
learning and the reinforcement learning. The supervised learning utilizes
trajectory optimization to provide the initial guidance to the policy, while
the reinforcement learning utilizes actor-critic algorithm to establish the
evaluation system even the supervisor is not accurate. The proposed learning
framework is more efficient compared with the reinforcement learning and
achieves better stability performance than the supervised learning. The
effectiveness of the method is verified by both the simulation and experiment.
A Learning Framework for Robust Bin Picking by Customized Grippers
Authors: Yongxiang Fan, Hsien-Chung Lin, Te Tang, Masayoshi Tomizuka
Date: 2018-09-23 00:00:00
Tasks: Collision Avoidance

Customized grippers have specifically designed fingers to increase the
contact area with the workpieces and improve the grasp robustness. However,
grasp planning for customized grippers is challenging due to the object
variations, surface contacts and structural constraints of the grippers. In
this paper, we propose a learning framework to plan robust grasps for
customized grippers in real-time. The learning framework contains a low-level
optimization-based planner to search for optimal grasps locally under object
shape variations, and a high-level learning-based explorer to learn the grasp
exploration based on previous grasp experience. The optimization-based planner
uses an iterative surface fitting (ISF) to simultaneously search for optimal
gripper transformation and finger displacement by minimizing the surface
fitting error. The high-level learning-based explorer trains a region-based
convolutional neural network (R-CNN) to propose good optimization regions,
which avoids ISF getting stuck in bad local optima and improves the collision
avoidance performance. The proposed learning framework with RCNN-ISF is able to
consider the structural constraints of the gripper, learn grasp exploration
strategy from previous experience, and plan optimal grasps in clutter
environment in real-time. The effectiveness of the algorithm is verified by
experiments.
Bounding Box Regression with Uncertainty for Accurate Object Detection
Authors: Yihui He, Chenchen Zhu, Jianren Wang, Marios Savvides, Xiangyu Zhang
Date: 2018-09-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, object-detection, Object Detection, Object Localization, regression

Large-scale object detection datasets (e.g., MS-COCO) try to define the
ground truth bounding boxes as clear as possible. However, we observe that
ambiguities are still introduced when labeling the bounding boxes. In this
paper, we propose a novel bounding box regression loss for learning bounding
box transformation and localization variance together. Our loss greatly
improves the localization accuracies of various architectures with nearly no
additional computation. The learned localization variance allows us to merge
neighboring bounding boxes during non-maximum suppression (NMS), which further
improves the localization performance. On MS-COCO, we boost the Average
Precision (AP) of VGG-16 Faster R-CNN from 23.6% to 29.1%. More importantly,
for ResNet-50-FPN Mask R-CNN, our method improves the AP and AP90 by 1.8% and
6.2% respectively, which significantly outperforms previous state-of-the-art
bounding box refinement methods. Our code and models are available at:
github.com/yihui-he/KL-Loss
DT-LET: Deep Transfer Learning by Exploring where to Transfer
Authors: Jianzhe Lin, Qi. Wang, Rabab Ward, Z. Jane Wang
Date: 2018-09-23 00:00:00
Tasks: Transfer Learning

Previous transfer learning methods based on deep network assume the knowledge
should be transferred between the same hidden layers of the source domain and
the target domains. This assumption doesn't always hold true, especially when
the data from the two domains are heterogeneous with different resolutions. In
such case, the most suitable numbers of layers for the source domain data and
the target domain data would differ. As a result, the high level knowledge from
the source domain would be transferred to the wrong layer of target domain.
Based on this observation, "where to transfer" proposed in this paper should be
a novel research frontier. We propose a new mathematic model named DT-LET to
solve this heterogeneous transfer learning problem. In order to select the best
matching of layers to transfer knowledge, we define specific loss function to
estimate the corresponding relationship between high-level features of data in
the source domain and the target domain. To verify this proposed cross-layer
model, experiments for two cross-domain recognition/classification tasks are
conducted, and the achieved superior results demonstrate the necessity of layer
correspondence searching.
Provably Correct Automatic Subdifferentiation for Qualified Programs
Authors: Sham Kakade, Jason D. Lee
Date: 2018-09-23 00:00:00

The Cheap Gradient Principle (Griewank 2008) --- the computational cost of
computing the gradient of a scalar-valued function is nearly the same (often
within a factor of $5$) as that of simply computing the function itself --- is
of central importance in optimization; it allows us to quickly obtain (high
dimensional) gradients of scalar loss functions which are subsequently used in
black box gradient-based optimization procedures. The current state of affairs
is markedly different with regards to computing subderivatives: widely used ML
libraries, including TensorFlow and PyTorch, do not correctly compute
(generalized) subderivatives even on simple examples. This work considers the
question: is there a Cheap Subgradient Principle? Our main result shows that,
under certain restrictions on our library of nonsmooth functions (standard in
nonlinear programming), provably correct generalized subderivatives can be
computed at a computational cost that is within a (dimension-free) factor of
$6$ of the cost of computing the scalar function itself.
Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization
Authors: Bao Wang, Alex T. Lin, Wei Zhu, Penghang Yin, Andrea L. Bertozzi, Stanley J. Osher
Date: 2018-09-23 00:00:00
Tasks: Adversarial Attack, Adversarial Defense, Data Augmentation

We improve the robustness of Deep Neural Net (DNN) to adversarial attacks by using an interpolating function as the output activation. This data-dependent activation remarkably improves both the generalization and robustness of DNN. In the CIFAR10 benchmark, we raise the robust accuracy of the adversarially trained ResNet20 from $\sim 46\%$ to $\sim 69\%$ under the state-of-the-art Iterative Fast Gradient Sign Method (IFGSM) based adversarial attack. When we combine this data-dependent activation with total variation minimization on adversarial images and training data augmentation, we achieve an improvement in robust accuracy by 38.9$\%$ for ResNet56 under the strongest IFGSM attack. Furthermore, We provide an intuitive explanation of our defense by analyzing the geometry of the feature space.
Towards Language Agnostic Universal Representations
Authors: Armen Aghajanyan, Xia Song, Saurabh Tiwary
Date: 2018-09-23 00:00:00
Proceeding: ACL 2019 7
Tasks: Math

When a bilingual student learns to solve word problems in math, we expect the
student to be able to solve these problem in both languages the student is
fluent in,even if the math lessons were only taught in one language. However,
current representations in machine learning are language dependent. In this
work, we present a method to decouple the language from the problem by learning
language agnostic representations and therefore allowing training a model in
one language and applying to a different one in a zero shot fashion. We learn
these representations by taking inspiration from linguistics and formalizing
Universal Grammar as an optimization process (Chomsky, 2014; Montague, 1970).
We demonstrate the capabilities of these representations by showing that the
models trained on a single language using language agnostic representations
achieve very similar accuracies in other languages.
A Train Status Assistant for Indian Railways
Authors: Himadri Mishra, Ramashish Gaurav, Biplav Srivastava
Date: 2018-09-23 00:00:00

Trains are part-and-parcel of every day lives in countries with large,
diverse, multi-lingual population like India. Consequently, an assistant which
can accurately predict and explain train delays will help people and businesses
alike. We present a novel conversation agent which can engage with people about
train status and inform them about its delay at in-line stations. It is trained
on past delay data from a subset of trains and generalizes to others.
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
Authors: Bichen Wu, Xuanyu Zhou, Sicheng Zhao, Xiangyu Yue, Kurt Keutzer
Date: 2018-09-22 00:00:00
Tasks: 3D Semantic Segmentation, Domain Adaptation, Point Cloud Segmentation, Robust 3D Semantic Segmentation, Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

Earlier work demonstrates the promise of deep-learning-based approaches for
point cloud segmentation; however, these approaches need to be improved to be
practically useful. To this end, we introduce a new model SqueezeSegV2 that is
more robust to dropout noise in LiDAR point clouds. With improved model
structure, training loss, batch normalization and additional input channel,
SqueezeSegV2 achieves significant accuracy improvement when trained on real
data. Training models for point cloud segmentation requires large amounts of
labeled point-cloud data, which is expensive to obtain. To sidestep the cost of
collection and annotation, simulators such as GTA-V can be used to create
unlimited amounts of labeled, synthetic data. However, due to domain shift,
models trained on synthetic data often do not generalize well to the real
world. We address this problem with a domain-adaptation training pipeline
consisting of three major components: 1) learned intensity rendering, 2)
geodesic correlation alignment, and 3) progressive domain calibration. When
trained on real data, our new model exhibits segmentation accuracy improvements
of 6.0-8.6% over the original SqueezeSeg. When training our new model on
synthetic data using the proposed domain adaptation pipeline, we nearly double
test accuracy on real-world data, from 29.0% to 57.4%. Our source code and
synthetic dataset will be open-sourced.
SelfKin: Self Adjusted Deep Model For Kinship Verification
Authors: Eran Dahan, Yosi Keller
Date: 2018-09-22 00:00:00
Tasks: Face Recognition, Kinship Verification, model, Self-Learning

One of the unsolved challenges in the field of biometrics and face
recognition is Kinship Verification. This problem aims to understand if two
people are family-related and how (sisters, brothers, etc.) Solving this
problem can give rise to varied tasks and applications. In the area of homeland
security (HLS) it is crucial to auto-detect if the person questioned is related
to a wanted suspect, In the field of biometrics, kinship-verification can help
to discriminate between families by photos and in the field of predicting or
fashion it can help to predict an older or younger model of people faces.
Lately, and with the advanced deep learning technology, this problem has gained
focus from the research community in matters of data and research. In this
article, we propose using a Deep Learning approach for solving the
Kinship-Verification problem. Further, we offer a novel self-learning deep
model, which learns the essential features from different faces. We show that
our model wins the Recognize Families In the Wild(RFIW2018,FG2018) challenge
and obtains state-of-the-art results. Moreover, we show that our proposed model
can reduce the size of the network by half without loss in performance.
Parametric Synthesis of Text on Stylized Backgrounds using PGGANs
Authors: Mayank Gupta, Abhinav Kumar, Sriganesh Madhvanath
Date: 2018-09-22 00:00:00
Tasks: Image Retrieval, License Plate Recognition, Retrieval

We describe a novel method of generating high-resolution real-world images of
text where the style and textual content of the images are described
parametrically. Our method combines text to image retrieval techniques with
progressive growing of Generative Adversarial Networks (PGGANs) to achieve
conditional generation of photo-realistic images that reflect specific styles,
as well as artifacts seen in real-world images. We demonstrate our method in
the context of automotive license plates. We assess the impact of varying the
number of training images of each style on the fidelity of the generated style,
and demonstrate the quality of the generated images using license plate
recognition systems.
Shift-based Primitives for Efficient Convolutional Neural Networks
Authors: Huasong Zhong, Xianggen Liu, Yihui He, Yuchun Ma
Date: 2018-09-22 00:00:00
Tasks: GPU

We propose a collection of three shift-based primitives for building
efficient compact CNN-based networks. These three primitives (channel shift,
address shift, shortcut shift) can reduce the inference time on GPU while
maintains the prediction accuracy. These shift-based primitives only moves the
pointer but avoids memory copy, thus very fast. For example, the channel shift
operation is 12.7x faster compared to channel shuffle in ShuffleNet but
achieves the same accuracy. The address shift and channel shift can be merged
into the point-wise group convolution and invokes only a single kernel call,
taking little time to perform spatial convolution and channel shift. Shortcut
shift requires no time to realize residual connection through allocating space
in advance. We blend these shift-based primitives with point-wise group
convolution and built two inference-efficient CNN architectures named
AddressNet and Enhanced AddressNet. Experiments on CIFAR100 and ImageNet
datasets show that our models are faster and achieve comparable or better
accuracy.
Artistic Instance-Aware Image Filtering by Convolutional Neural Networks
Authors: Milad Tehrani, Mahnoosh Bagheri, Mahdi Ahmadi, Alireza Norouzi, Nader Karimi, Shadrokh Samavi
Date: 2018-09-22 00:00:00
Tasks: Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

In the recent years, public use of artistic effects for editing and
beautifying images has encouraged researchers to look for new approaches to
this task. Most of the existing methods apply artistic effects to the whole
image. Exploitation of neural network vision technologies like object detection
and semantic segmentation could be a new viewpoint in this area. In this paper,
we utilize an instance segmentation neural network to obtain a class mask for
separately filtering the background and foreground of an image. We implement a
top prior-mask selection to let us select an object class for filtering
purpose. Different artistic effects are used in the filtering process to meet
the requirements of a vast variety of users. Also, our method is flexible
enough to allow the addition of new filters. We use pre-trained Mask R-CNN
instance segmentation on the COCO dataset as the segmentation network.
Experimental results on the use of different filters are performed. System's
output results show that this novel approach can create satisfying artistic
images with fast operation and simple interface.
Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning
Authors: Justus T. C. Schwabedal, Daniel Sippel, Moritz D. Brandt, Stephan Bialonski
Date: 2018-09-22 00:00:00
Tasks: EEG, EEG Artifact Removal, Electroencephalogram (EEG), General Classification, Sleep Stage Detection

Sleep scoring is a necessary and time-consuming task in sleep studies. In
animal models (such as mice) or in humans, automating this tedious process
promises to facilitate long-term studies and to promote sleep biology as a
data-driven field. We introduce a deep neural network model that is able to
predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG
and EMG recordings with excellent scoring results for out-of-sample data.
Predictions are made on epochs of 4 seconds length, and epochs are classified
as artifact-free or not. The model architecture draws on recent advances in
deep learning and in convolutional neural networks research. In contrast to
previous approaches towards automated sleep scoring, our model does not rely on
manually defined features of the data but learns predictive features
automatically. We expect deep learning models like ours to become widely
applied in different fields, automating many repetitive cognitive tasks that
were previously difficult to tackle.