pwc-archive/papers-with-abstracts huggingface dataset:
SlowFast Networks for Video Recognition
Authors: Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, Kaiming He
Date: 2018-12-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Classification, Action Detection, Action Recognition, Action Recognition In Videos, General Classification, Video Recognition

We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway, operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating at high frame rate, to capture motion at fine temporal resolution. The Fast pathway can be made very lightweight by reducing its channel capacity, yet can learn useful temporal information for video recognition. Our models achieve strong performance for both action classification and detection in video, and large improvements are pin-pointed as contributions by our SlowFast concept. We report state-of-the-art accuracy on major video recognition benchmarks, Kinetics, Charades and AVA. Code has been made available at: https://github.com/facebookresearch/SlowFast
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization
Authors: Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu
Date: 2018-12-10 00:00:00
Proceeding: ICLR 2019 5

Batch Normalization (BN) has become a cornerstone of deep learning across
diverse architectures, appearing to help optimization as well as
generalization. While the idea makes intuitive sense, theoretical analysis of
its effectiveness has been lacking. Here theoretical support is provided for
one of its conjectured properties, namely, the ability to allow gradient
descent to succeed with less tuning of learning rates. It is shown that even if
we fix the learning rate of scale-invariant parameters (e.g., weights of each
layer with BN) to a constant (say, $0.3$), gradient descent still approaches a
stationary point (i.e., a solution where gradient is zero) in the rate of
$T^{-1/2}$ in $T$ iterations, asymptotically matching the best bound for
gradient descent with well-tuned learning rates. A similar result with
convergence rate $T^{-1/4}$ is also shown for stochastic gradient descent.
Building Ethically Bounded AI
Authors: Francesca Rossi, Nicholas Mattei
Date: 2018-12-10 00:00:00
Tasks: Decision Making, Fairness

The more AI agents are deployed in scenarios with possibly unexpected
situations, the more they need to be flexible, adaptive, and creative in
achieving the goal we have given them. Thus, a certain level of freedom to
choose the best path to the goal is inherent in making AI robust and flexible
enough. At the same time, however, the pervasive deployment of AI in our life,
whether AI is autonomous or collaborating with humans, raises several ethical
challenges. AI agents should be aware and follow appropriate ethical principles
and should thus exhibit properties such as fairness or other virtues. These
ethical principles should define the boundaries of AI's freedom and creativity.
However, it is still a challenge to understand how to specify and reason with
ethical boundaries in AI agents and how to combine them appropriately with
subjective preferences and goal specifications. Some initial attempts employ
either a data-driven example-based approach for both, or a symbolic rule-based
approach for both. We envision a modular approach where any AI technique can be
used for any of these essential ingredients in decision making or decision
support systems, paired with a contextual approach to define their combination
and relative weight. In a world where neither humans nor AI systems work in
isolation, but are tightly interconnected, e.g., the Internet of Things, we
also envision a compositional approach to building ethically bounded AI, where
the ethical properties of each component can be fruitfully exploited to derive
those of the overall system. In this paper we define and motivate the notion of
ethically-bounded AI, we describe two concrete examples, and we outline some
outstanding challenges.
Accuracy, Uncertainty, and Adaptability of Automatic Myocardial ASL Segmentation using Deep CNN
Authors: Hung P. Do, Yi Guo, Andrew J. Yoon, Krishna S. Nayak
Date: 2018-12-10 00:00:00
Tasks: Diagnostic, Segmentation

PURPOSE: To apply deep CNN to the segmentation task in myocardial arterial spin labeled (ASL) perfusion imaging and to develop methods that measure uncertainty and that adapt the CNN model to a specific false positive vs. false negative tradeoff. METHODS: The Monte Carlo dropout (MCD) U-Net was trained on data from 22 subjects and tested on data from 6 heart transplant recipients. Manual segmentation and regional myocardial blood flow (MBF) were available for comparison. We consider two global uncertainty measures, named Dice Uncertainty and MCD Uncertainty, which were calculated with and without the use of manual segmentation, respectively. Tversky loss function with a hyperparameter $\beta$ was used to adapt the model to a specific false positive vs. false negative tradeoff. RESULTS: The MCD U-Net achieved Dice coefficient of mean(std) = 0.91(0.04) on the test set. MBF measured using automatic segmentations was highly correlated to that measured using the manual segmentation ($R^2$ = 0.96). Dice Uncertainty and MCD Uncertainty were in good agreement ($R^2$ = 0.64). As $\beta$ increased, the false positive rate systematically decreased and false negative rate systematically increased. CONCLUSION: We demonstrate the feasibility of deep CNN for automatic segmentation of myocardial ASL, with good accuracy. We also introduce two simple methods for assessing model uncertainty. Finally, we demonstrate the ability to adapt the CNN model to a specific false positive vs. false negative tradeoff. These findings are directly relevant to automatic segmentation in quantitative cardiac MRI and are broadly applicable to automatic segmentation problems in diagnostic imaging.
Bayesian Layers: A Module for Neural Network Uncertainty
Authors: Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner
Date: 2018-12-10 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Gaussian Processes, Machine Translation, Probabilistic Programming, Translation

We describe Bayesian Layers, a module designed for fast experimentation with
neural network uncertainty. It extends neural network libraries with drop-in
replacements for common layers. This enables composition via a unified
abstraction over deterministic and stochastic functions and allows for
scalability via the underlying system. These layers capture uncertainty over
weights (Bayesian neural nets), pre-activation units (dropout), activations
("stochastic output layers"), or the function itself (Gaussian processes). They
can also be reversible to propagate uncertainty from input to output. We
include code examples for common architectures such as Bayesian LSTMs, deep
GPs, and flow-based models. As demonstration, we fit a 5-billion parameter
"Bayesian Transformer" on 512 TPUv2 cores for uncertainty in machine
translation and a Bayesian dynamics model for model-based planning. Finally, we
show how Bayesian Layers can be used within the Edward2 probabilistic
programming language for probabilistic programs with stochastic processes.
Guided Dropout
Authors: Rohit Keshari, Richa Singh, Mayank Vatsa
Date: 2018-12-10 00:00:00

Dropout is often used in deep neural networks to prevent over-fitting.
Conventionally, dropout training invokes \textit{random drop} of nodes from the
hidden layers of a Neural Network. It is our hypothesis that a guided selection
of nodes for intelligent dropout can lead to better generalization as compared
to the traditional dropout. In this research, we propose "guided dropout" for
training deep neural network which drop nodes by measuring the strength of each
node. We also demonstrate that conventional dropout is a specific case of the
proposed guided dropout. Experimental evaluation on multiple datasets including
MNIST, CIFAR10, CIFAR100, SVHN, and Tiny ImageNet demonstrate the efficacy of
the proposed guided dropout.
Disentangled Dynamic Representations from Unordered Data
Authors: Leonhard Helminger, Abdelaziz Djelouah, Markus Gross, Romann M. Weber
Date: 2018-12-10 00:00:00

We present a deep generative model that learns disentangled static and
dynamic representations of data from unordered input. Our approach exploits
regularities in sequential data that exist regardless of the order in which the
data is viewed. The result of our factorized graphical model is a
well-organized and coherent latent space for data dynamics. We demonstrate our
method on several synthetic dynamic datasets and real video data featuring
various facial expressions and head poses.
Improving Model-Based Control and Active Exploration with Reconstruction Uncertainty Optimization
Authors: Norman Di Palo, Harri Valpola
Date: 2018-12-10 00:00:00
Tasks: Active Learning

Model based predictions of future trajectories of a dynamical system often
suffer from inaccuracies, forcing model based control algorithms to re-plan
often, thus being computationally expensive, suboptimal and not reliable. In
this work, we propose a model agnostic method for estimating the uncertainty of
a model?s predictions based on reconstruction error, using it in control and
exploration. As our experiments show, this uncertainty estimation can be used
to improve control performance on a wide variety of environments by choosing
predictions of which the model is confident. It can also be used for active
learning to explore more efficiently the environment by planning for
trajectories with high uncertainty, allowing faster model learning.
An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles
Authors: Hadi Abdi Khojasteh, Alireza Abbas Alipour, Ebrahim Ansari, Parvin Razzaghi
Date: 2018-12-10 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decoder, Scene Understanding, Steering Control

Nowadays, automobile manufacturers make efforts to develop ways to make cars
fully safe. Monitoring driver's actions by computer vision techniques to detect
driving mistakes in real-time and then planning for autonomous driving to avoid
vehicle collisions is one of the most important issues that has been
investigated in the machine vision and Intelligent Transportation Systems
(ITS). The main goal of this study is to prevent accidents caused by fatigue,
drowsiness, and driver distraction. To avoid these incidents, this paper
proposes an integrated safety system that continuously monitors the driver's
attention and vehicle surroundings, and finally decides whether the actual
steering control status is safe or not. For this purpose, we equipped an
ordinary car called FARAZ with a vision system consisting of four mounted
cameras along with a universal car tool for communicating with surrounding
factory-installed sensors and other car systems, and sending commands to
actuators. The proposed system leverages a scene understanding pipeline using
deep convolutional encoder-decoder networks and a driver state detection
pipeline. We have been identifying and assessing domestic capabilities for the
development of technologies specifically of the ordinary vehicles in order to
manufacture smart cars and eke providing an intelligent system to increase
safety and to assist the driver in various conditions/situations.
A New Ensemble Learning Framework for 3D Biomedical Image Segmentation
Authors: Hao Zheng, Yizhe Zhang, Lin Yang, Peixian Liang, Zhuo Zhao, Chaoli Wang, Danny Z. Chen
Date: 2018-12-10 00:00:00
Tasks: 3D Medical Imaging Segmentation, Ensemble Learning, Image Segmentation, Segmentation, Semantic Segmentation

3D image segmentation plays an important role in biomedical image analysis.
Many 2D and 3D deep learning models have achieved state-of-the-art segmentation
performance on 3D biomedical image datasets. Yet, 2D and 3D models have their
own strengths and weaknesses, and by unifying them together, one may be able to
achieve more accurate results. In this paper, we propose a new ensemble
learning framework for 3D biomedical image segmentation that combines the
merits of 2D and 3D models. First, we develop a fully convolutional network
based meta-learner to learn how to improve the results from 2D and 3D models
(base-learners). Then, to minimize over-fitting for our sophisticated
meta-learner, we devise a new training method that uses the results of the
base-learners as multiple versions of "ground truths". Furthermore, since our
new meta-learner training scheme does not depend on manual annotation, it can
utilize abundant unlabeled 3D image data to further improve the model.
Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset
and the mouse piriform cortex dataset) show that our approach is effective
under fully-supervised, semi-supervised, and transductive settings, and attains
superior performance over state-of-the-art image segmentation methods.
Data Fine-tuning
Authors: Saheb Chhabra, Puspita Majumdar, Mayank Vatsa, Richa Singh
Date: 2018-12-10 00:00:00
Tasks: Attribute, Emotion Recognition, Face Recognition, General Classification

In real-world applications, commercial off-the-shelf systems are utilized for
performing automated facial analysis including face recognition, emotion
recognition, and attribute prediction. However, a majority of these commercial
systems act as black boxes due to the inaccessibility of the model parameters
which makes it challenging to fine-tune the models for specific applications.
Stimulated by the advances in adversarial perturbations, this research proposes
the concept of Data Fine-tuning to improve the classification accuracy of a
given model without changing the parameters of the model. This is accomplished
by modeling it as data (image) perturbation problem. A small amount of "noise"
is added to the input with the objective of minimizing the classification loss
without affecting the (visual) appearance. Experiments performed on three
publicly available datasets LFW, CelebA, and MUCT, demonstrate the
effectiveness of the proposed concept.
Stagewise Training Accelerates Convergence of Testing Error Over SGD
Authors: Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang
Date: 2018-12-10 00:00:00
Proceeding: NeurIPS 2019 12

Stagewise training strategy is widely used for learning neural networks,
which runs a stochastic algorithm (e.g., SGD) starting with a relatively large
step size (aka learning rate) and geometrically decreasing the step size after
a number of iterations. It has been observed that the stagewise SGD has much
faster convergence than the vanilla SGD with a polynomially decaying step size
in terms of both training error and testing error. {\it But how to explain this
phenomenon has been largely ignored by existing studies.} This paper provides
some theoretical evidence for explaining this faster convergence. In
particular, we consider a stagewise training strategy for minimizing empirical
risk that satisfies the Polyak-\L ojasiewicz (PL) condition, which has been
observed/proved for neural networks and also holds for a broad family of convex
functions. For convex loss functions and two classes of "nice-behaviored"
non-convex objectives that are close to a convex function, we establish faster
convergence of stagewise training than the vanilla SGD under the PL condition
on both training error and testing error. Experiments on stagewise learning of
deep residual networks exhibits that it satisfies one type of non-convexity
assumption and therefore can be explained by our theory. Of independent
interest, the testing error bounds for the considered non-convex loss functions
are dimensionality and norm independent.
An Introduction to Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
Authors: Hyeryung Jang, Osvaldo Simeone, Brian Gardner, André Grüning
Date: 2018-12-10 00:00:00
Tasks: Variational Inference

Spiking Neural Networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be leveraged by hardware implementations that have demonstrated significant energy reductions as compared to conventional Artificial Neural Networks (ANNs). Most existing training algorithms for SNNs have been designed either for biological plausibility or through conversion from pre-trained ANNs via rate encoding. This paper aims at providing an introduction to SNNs by focusing on a probabilistic signal processing methodology that enables the direct derivation of learning rules leveraging the unique time encoding capabilities of SNNs. To this end, the paper adopts discrete-time probabilistic models for networked spiking neurons, and it derives supervised and unsupervised learning rules from first principles by using variational inference. Examples and open research problems are also provided.
Learning Representations of Sets through Optimized Permutations
Authors: Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
Date: 2018-12-10 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Representations of sets are challenging to learn because operations on sets
should be permutation-invariant. To this end, we propose a
Permutation-Optimisation module that learns how to permute a set end-to-end.
The permuted set can be further processed to learn a permutation-invariant
representation of that set, avoiding a bottleneck in traditional set models. We
demonstrate our model's ability to learn permutations and set representations
with either explicit or implicit supervision on four datasets, on which we
achieve state-of-the-art results: number sorting, image mosaics, classification
from image mosaics, and visual question answering.
Non-Intrusive Load Monitoring with Fully Convolutional Networks
Authors: Cillian Brewitt, Nigel Goddard
Date: 2018-12-10 00:00:00
Tasks: Computational Efficiency, Non-Intrusive Load Monitoring

Non-intrusive load monitoring or energy disaggregation involves estimating
the power consumption of individual appliances from measurements of the total
power consumption of a home. Deep neural networks have been shown to be
effective for energy disaggregation. In this work, we present a deep neural
network architecture which achieves state of the art disaggregation performance
with substantially improved computational efficiency, reducing model training
time by a factor of 32 and prediction time by a factor of 43. This improvement
in efficiency could be especially useful for applications where disaggregation
must be performed in home on lower power devices, or for research experiments
which involve training a large number of models.
Self-Contained Stylization via Steganography for Reverse and Serial Style Transfer
Authors: Hung-Yu Chen, I-Sheng Fang, Wei-Chen Chiu
Date: 2018-12-10 00:00:00
Tasks: Reverse Style Transfer, Serial Style Transfer, Style Transfer

Style transfer has been widely applied to give real-world images a new artistic look. However, given a stylized image, the attempts to use typical style transfer methods for de-stylization or transferring it again into another style usually lead to artifacts or undesired results. We realize that these issues are originated from the content inconsistency between the original image and its stylized output. Therefore, in this paper we advance to keep the content information of the input image during the process of style transfer by the power of steganography, with two approaches proposed: a two-stage model and an end-to-end model. We conduct extensive experiments to successfully verify the capacity of our models, in which both of them are able to not only generate stylized images of quality comparable with the ones produced by typical style transfer methods, but also effectively eliminate the artifacts introduced in reconstructing original input from a stylized image as well as performing multiple times of style transfer in series.
Attention-guided Unified Network for Panoptic Segmentation
Authors: Yanwei Li, Xinze Chen, Zheng Zhu, Lingxi Xie, Guan Huang, Dalong Du, Xingang Wang
Date: 2018-12-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Panoptic Segmentation, Segmentation

This paper studies panoptic segmentation, a recently proposed task which
segments foreground (FG) objects at the instance level as well as background
(BG) contents at the semantic level. Existing methods mostly dealt with these
two problems separately, but in this paper, we reveal the underlying
relationship between them, in particular, FG objects provide complementary cues
to assist BG understanding. Our approach, named the Attention-guided Unified
Network (AUNet), is a unified framework with two branches for FG and BG
segmentation simultaneously. Two sources of attentions are added to the BG
branch, namely, RPN and FG segmentation mask to provide object-level and
pixel-level attentions, respectively. Our approach is generalized to different
backbones with consistent accuracy gain in both FG and BG segmentation, and
also sets new state-of-the-arts both in the MS-COCO (46.5% PQ) and Cityscapes
(59.0% PQ) benchmarks.
Physics-Based Learning for Robotic Environmental Sensing
Authors: Reza Khodayi-mehr, Michael M. Zavlanos
Date: 2018-12-10 00:00:00
Tasks: Bayesian Inference, Gaussian Processes, Model Selection

We propose a physics-based method to learn environmental fields (EFs) using a mobile robot. Common purely data-driven methods require prohibitively many measurements to accurately learn such complex EFs. Alternatively, physics-based models provide global knowledge of EFs but require experimental validation, depend on uncertain parameters, and are intractable for mobile robots. To address these challenges, we propose a Bayesian framework to select the most likely physics-based models of EFs in real-time, from a pool of numerical solutions generated offline as a function of the uncertain parameters. Specifically, we focus on turbulent flow fields and utilize Gaussian processes (GPs) to construct statistical models for them, using the pool of numerical solutions to inform their prior mean. To incorporate flow measurements into these GPs, we control a custom-built mobile robot through a sequence of waypoints that maximize the information content of the measurements. We experimentally demonstrate that our proposed framework constructs a posterior distribution of the flow field that better approximates the real flow compared to the prior numerical solutions and purely data-driven methods.
Regularization by architecture: A deep prior approach for inverse problems
Authors: Sören Dittmer, Tobias Kluth, Peter Maass, Daniel Otero Baguer
Date: 2018-12-10 00:00:00

The present paper studies so-called deep image prior (DIP) techniques in the context of ill-posed inverse problems. DIP networks have been recently introduced for applications in image processing; also first experimental results for applying DIP to inverse problems have been reported. This paper aims at discussing different interpretations of DIP and to obtain analytic results for specific network designs and linear operators. The main contribution is to introduce the idea of viewing these approaches as the optimization of Tikhonov functionals rather than optimizing networks. Besides theoretical results, we present numerical verifications.