pwc-archive/papers-with-abstracts huggingface dataset:
Cross-Modal Distillation for RGB-Depth Person Re-Identification
Authors: Frank Hafner, Amran Bhuiyan, Julian F. P. Kooij, Eric Granger
Date: 2018-10-27 00:00:00
Tasks: Autonomous Driving, Cross-Modal  Person Re-Identification, Cross-Modal Person Re-Identification, object-detection, Object Detection, Person Re-Identification, Transfer Learning

Person re-identification is a key challenge for surveillance across multiple sensors. Prompted by the advent of powerful deep learning models for visual recognition, and inexpensive RGB-D cameras and sensor-rich mobile robotic platforms, e.g. self-driving vehicles, we investigate the relatively unexplored problem of cross-modal re-identification of persons between RGB (color) and depth images. The considerable divergence in data distributions across different sensor modalities introduces additional challenges to the typical difficulties like distinct viewpoints, occlusions, and pose and illumination variation. While some work has investigated re-identification across RGB and infrared, we take inspiration from successes in transfer learning from RGB to depth in object detection tasks. Our main contribution is a novel method for cross-modal distillation for robust person re-identification, which learns a shared feature representation space of person's appearance in both RGB and depth images. In addition, we propose a cross-modal attention mechanism where the gating signal from one modality can dynamically activate the most discriminant CNN filters of the other modality. The proposed distillation method is compared to conventional and deep learning approaches proposed for other cross-domain re-identification tasks. Results obtained on the public BIWI and RobotPKU datasets indicate that the proposed method can significantly outperform the state-of-the-art approaches by up to 16.1% in mean Average Precision (mAP), demonstrating the benefit of the distillation paradigm. The experimental results also indicate that using cross-modal attention allows to improve recognition accuracy considerably with respect to the proposed distillation method and relevant state-of-the-art approaches.
Object-Oriented Dynamics Predictor
Authors: Guangxiang Zhu, Zhiao Huang, Chongjie Zhang
Date: 2018-05-25 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Model-based Reinforcement Learning, Object, Reinforcement Learning

Generalization has been one of the major challenges for learning dynamics
models in model-based reinforcement learning. However, previous work on
action-conditioned dynamics prediction focuses on learning the pixel-level
motion and thus does not generalize well to novel environments with different
object layouts. In this paper, we present a novel object-oriented framework,
called object-oriented dynamics predictor (OODP), which decomposes the
environment into objects and predicts the dynamics of objects conditioned on
both actions and object-to-object relations. It is an end-to-end neural network
and can be trained in an unsupervised manner. To enable the generalization
ability of dynamics learning, we design a novel CNN-based relation mechanism
that is class-specific (rather than object-specific) and exploits the locality
principle. Empirical results show that OODP significantly outperforms previous
methods in terms of generalization over novel environments with various object
layouts. OODP is able to learn from very few environments and accurately
predict dynamics in a large number of unseen environments. In addition, OODP
learns semantically and visually interpretable dynamics models.
Informative Features for Model Comparison
Authors: Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton
Date: 2018-10-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: model

Given two candidate models, and a set of target observations, we address the
problem of measuring the relative goodness of fit of the two models. We propose
two new statistical tests which are nonparametric, computationally efficient
(runtime complexity is linear in the sample size), and interpretable. As a
unique advantage, our tests can produce a set of examples (informative
features) indicating the regions in the data domain where one model fits
significantly better than the other. In a real-world problem of comparing GAN
models, the test power of our new test matches that of the state-of-the-art
test of relative goodness of fit, while being one order of magnitude faster.
Time series clustering based on the characterisation of segment typologies
Authors: David Guijo-Rubio, Antonio Manuel Durán-Rosal, Pedro Antonio Gutiérrez, Alicia Troncoso, César Hervás-Martínez
Date: 2018-10-27 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis, Time Series Classification, Time Series Clustering

Time series clustering is the process of grouping time series with respect to
their similarity or characteristics. Previous approaches usually combine a
specific distance measure for time series and a standard clustering method.
However, these approaches do not take the similarity of the different
subsequences of each time series into account, which can be used to better
compare the time series objects of the dataset. In this paper, we propose a
novel technique of time series clustering based on two clustering stages. In a
first step, a least squares polynomial segmentation procedure is applied to
each time series, which is based on a growing window technique that returns
different-length segments. Then, all the segments are projected into same
dimensional space, based on the coefficients of the model that approximates the
segment and a set of statistical features. After mapping, a first hierarchical
clustering phase is applied to all mapped segments, returning groups of
segments for each time series. These clusters are used to represent all time
series in the same dimensional space, after defining another specific mapping
process. In a second and final clustering stage, all the time series objects
are grouped. We consider internal clustering quality to automatically adjust
the main parameter of the algorithm, which is an error threshold for the
segmenta- tion. The results obtained on 84 datasets from the UCR Time Series
Classification Archive have been compared against two state-of-the-art methods,
showing that the performance of this methodology is very promising.
Deep learning for denoising
Authors: Siwei Yu, Jianwei Ma, Wenlong Wang
Date: 2018-10-27 00:00:00
Tasks: Deep Learning, Denoising

Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set, where the inputs are the raw datasets and the corresponding outputs are the desired clean data. After the completion of training, the deep learning method achieves adaptive denoising with no requirements of (i) accurate modelings of the signal and noise, or (ii) optimal parameters tuning. We call this intelligent denoising. We use a convolutional neural network as the basic tool for deep learning. In random and linear noise attenuation, the training set is generated with artificially added noise. In the multiple attenuation step, the training set is generated with acoustic wave equation. Stochastic gradient descent is used to solve the optimal parameters for the convolutional neural network. The runtime of deep learning on a graphics processing unit for denoising has the same order as the $f-x$ deconvolution method. Synthetic and field results show the potential applications of deep learning in automatic attenuation of random noise (with unknown variance), linear noise, and multiples.
Handling Imbalanced Dataset in Multi-label Text Categorization using Bagging and Adaptive Boosting
Authors: Genta Indra Winata, Masayu Leylia Khodra
Date: 2018-10-27 00:00:00
Tasks: Text Categorization

Imbalanced dataset is occurred due to uneven distribution of data available in the real world such as disposition of complaints on government offices in Bandung. Consequently, multi-label text categorization algorithms may not produce the best performance because classifiers tend to be weighed down by the majority of the data and ignore the minority. In this paper, Bagging and Adaptive Boosting algorithms are employed to handle the issue and improve the performance of text categorization. The result is evaluated with four evaluation metrics such as hamming loss, subset accuracy, example-based accuracy and micro-averaged f-measure. Bagging ML-LP with SMO weak classifier is the best performer in terms of subset accuracy and example-based accuracy. Bagging ML-BR with SMO weak classifier has the best micro-averaged f-measure among all. In other hand, AdaBoost MH with J48 weak classifier has the lowest hamming loss value. Thus, both algorithms have high potential in boosting the performance of text categorization, but only for certain weak classifiers. However, bagging has more potential than adaptive boosting in increasing the accuracy of minority labels.
Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis
Authors: Haoye Dong, Xiaodan Liang, Ke Gong, Hanjiang Lai, Jia Zhu, Jian Yin
Date: 2018-10-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Generative Adversarial Network, Image Generation

Despite remarkable advances in image synthesis research, existing works often
fail in manipulating images under the context of large geometric
transformations. Synthesizing person images conditioned on arbitrary poses is
one of the most representative examples where the generation quality largely
relies on the capability of identifying and modeling arbitrary transformations
on different body parts. Current generative models are often built on local
convolutions and overlook the key challenges (e.g. heavy occlusions, different
views or dramatic appearance changes) when distinct geometric changes happen
for each part, caused by arbitrary pose manipulations. This paper aims to
resolve these challenges induced by geometric variability and spatial
displacements via a new Soft-Gated Warping Generative Adversarial Network
(Warping-GAN), which is composed of two stages: 1) it first synthesizes a
target part segmentation map given a target pose, which depicts the
region-level spatial layouts for guiding image synthesis with higher-level
structure constraints; 2) the Warping-GAN equipped with a soft-gated
warping-block learns feature-level mapping to render textures from the original
image into the generated segmentation map. Warping-GAN is capable of
controlling different transformation degrees given distinct target poses.
Moreover, the proposed warping-block is light-weight and flexible enough to be
injected into any networks. Human perceptual studies and quantitative
evaluations demonstrate the superiority of our Warping-GAN that significantly
outperforms all existing methods on two large datasets.
A Miniaturized Semantic Segmentation Method for Remote Sensing Image
Authors: Shou-Yu Chen, Guang-Sheng Chen, Wei-Peng Jing
Date: 2018-10-27 00:00:00
Tasks: Model Compression, Semantic Segmentation

In order to save the memory, we propose a miniaturization method for neural
network to reduce the parameter quantity existed in remote sensing (RS) image
semantic segmentation model. The compact convolution optimization method is
first used for standard U-Net to reduce the weights quantity. With the purpose
of decreasing model performance loss caused by miniaturization and based on the
characteristics of remote sensing image, fewer down-samplings and improved
cascade atrous convolution are then used to improve the performance of the
miniaturized U-Net. Compared with U-Net, our proposed Micro-Net not only
achieves 29.26 times model compression, but also basically maintains the
performance unchanged on the public dataset. We provide a Keras and Tensorflow
hybrid programming implementation for our model:
https://github.com/Isnot2bad/Micro-Net
Self-Supervised GAN to Counter Forgetting
Authors: Ting Chen, Xiaohua Zhai, Neil Houlsby
Date: 2018-10-27 00:00:00
Tasks: Continual Learning, General Classification

GANs involve training two networks in an adversarial game, where each
network's task depends on its adversary. Recently, several works have framed
GAN training as an online or continual learning problem. We focus on the
discriminator, which must perform classification under an (adversarially)
shifting data distribution. When trained on sequential tasks, neural networks
exhibit \emph{forgetting}. For GANs, discriminator forgetting leads to training
instability. To counter forgetting, we encourage the discriminator to maintain
useful representations by adding a self-supervision. Conditional GANs have a
similar effect using labels. However, our self-supervised GAN does not require
labels, and closes the performance gap between conditional and unconditional
models. We show that, in doing so, the self-supervised discriminator learns
better representations than regular GANs.
Nonlocal flocking dynamics: Learning the fractional order of PDEs from particle simulations
Authors: Zhiping Mao, Zhen Li, George Em. Karniadakis
Date: 2018-10-27 00:00:00
Tasks: Bayesian Optimization

Flocking refers to collective behavior of a large number of interacting
entities, where the interactions between discrete individuals produce
collective motion on the large scale. We employ an agent-based model to
describe the microscopic dynamics of each individual in a flock, and use a
fractional PDE to model the evolution of macroscopic quantities of interest.
The macroscopic models with phenomenological interaction functions are derived
by applying the continuum hypothesis to the microscopic model. Instead of
specifying the fPDEs with an ad hoc fractional order for nonlocal flocking
dynamics, we learn the effective nonlocal influence function in fPDEs directly
from particle trajectories generated by the agent-based simulations. We
demonstrate how the learning framework is used to connect the discrete
agent-based model to the continuum fPDEs in 1D and 2D nonlocal flocking
dynamics. In particular, a Cucker-Smale particle model is employed to describe
the microscale dynamics of each individual, while Euler equations with nonlocal
interaction terms are used to compute the evolution of macroscale quantities.
The trajectories generated by the particle simulations mimic the field data of
tracking logs that can be obtained experimentally. They can be used to learn
the fractional order of the influence function using a Gaussian process
regression model implemented with the Bayesian optimization. We show that the
numerical solution of the learned Euler equations solved by the finite volume
scheme can yield correct density distributions consistent with the collective
behavior of the agent-based system. The proposed method offers new insights on
how to scale the discrete agent-based models to the continuum-based PDE models,
and could serve as a paradigm on extracting effective governing equations for
nonlocal flocking dynamics directly from particle trajectories.
Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks
Authors: Azadeh Sadat Mozafari, Hugo Siqueira Gomes, Wilson Leão, Steeven Janny, Christian Gagné
Date: 2018-10-27 00:00:00
Tasks: Autonomous Driving, Decision Making, Lesion Detection

Recently, Deep Neural Networks (DNNs) have been achieving impressive results on wide range of tasks. However, they suffer from being well-calibrated. In decision-making applications, such as autonomous driving or medical diagnosing, the confidence of deep networks plays an important role to bring the trust and reliability to the system. To calibrate the deep networks' confidence, many probabilistic and measure-based approaches are proposed. Temperature Scaling (TS) is a state-of-the-art among measure-based calibration methods which has low time and memory complexity as well as effectiveness. In this paper, we study TS and show it does not work properly when the validation set that TS uses for calibration has small size or contains noisy-labeled samples. TS also cannot calibrate highly accurate networks as well as non-highly accurate ones. Accordingly, we propose Attended Temperature Scaling (ATS) which preserves the advantages of TS while improves calibration in aforementioned challenging situations. We provide theoretical justifications for ATS and assess its effectiveness on wide range of deep models and datasets. We also compare the calibration results of TS and ATS on skin lesion detection application as a practical problem where well-calibrated system can play important role in making a decision.
Learning Abstract Options
Authors: Matthew Riemer, Miao Liu, Gerald Tesauro
Date: 2018-10-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Reinforcement Learning

Building systems that autonomously create temporal abstractions from data is a key challenge in scaling learning and planning in reinforcement learning. One popular approach for addressing this challenge is the options framework (Sutton et al., 1999). However, only recently in (Bacon et al., 2017) was a policy gradient theorem derived for online learning of general purpose options in an end to end fashion. In this work, we extend previous work on this topic that only focuses on learning a two-level hierarchy including options and primitive actions to enable learning simultaneously at multiple resolutions in time. We achieve this by considering an arbitrarily deep hierarchy of options where high level temporally extended options are composed of lower level options with finer resolutions in time. We extend results from (Bacon et al., 2017) and derive policy gradient theorems for a deep hierarchy of options. Our proposed hierarchical option-critic architecture is capable of learning internal policies, termination conditions, and hierarchical compositions over options without the need for any intrinsic rewards or subgoals. Our empirical results in both discrete and continuous environments demonstrate the efficiency of our framework.
Gradient-Free Learning Based on the Kernel and the Range Space
Authors: Kar-Ann Toh, Zhiping Lin, Zhengguo Li, Beomseok Oh, Lei Sun
Date: 2018-10-27 00:00:00

In this article, we show that solving the system of linear equations by
manipulating the kernel and the range space is equivalent to solving the
problem of least squares error approximation. This establishes the ground for a
gradient-free learning search when the system can be expressed in the form of a
linear matrix equation. When the nonlinear activation function is invertible,
the learning problem of a fully-connected multilayer feedforward neural network
can be easily adapted for this novel learning framework. By a series of kernel
and range space manipulations, it turns out that such a network learning boils
down to solving a set of cross-coupling equations. By having the weights
randomly initialized, the equations can be decoupled and the network solution
shows relatively good learning capability for real world data sets of small to
moderate dimensions. Based on the structural information of the matrix
equation, the network representation is found to be dependent on the number of
data samples and the output dimension.
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples
Authors: Guanhong Tao, Shiqing Ma, Yingqi Liu, Xiangyu Zhang
Date: 2018-10-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Attribute, Face Recognition, General Classification

Adversarial sample attacks perturb benign inputs to induce DNN misbehaviors.
Recent research has demonstrated the widespread presence and the devastating
consequences of such attacks. Existing defense techniques either assume prior
knowledge of specific attacks or may not work well on complex models due to
their underlying assumptions. We argue that adversarial sample attacks are
deeply entangled with interpretability of DNN models: while classification
results on benign inputs can be reasoned based on the human perceptible
features/attributes, results on adversarial samples can hardly be explained.
Therefore, we propose a novel adversarial sample detection technique for face
recognition models, based on interpretability. It features a novel
bi-directional correspondence inference between attributes and internal neurons
to identify neurons critical for individual attributes. The activation values
of critical neurons are enhanced to amplify the reasoning part of the
computation and the values of other neurons are weakened to suppress the
uninterpretable part. The classification results after such transformation are
compared with those of the original model to detect adversaries. Results show
that our technique can achieve 94% detection accuracy for 7 different kinds of
attacks with 9.91% false positives on benign inputs. In contrast, a
state-of-the-art feature squeezing technique can only achieve 55% accuracy with
23.3% false positives.
$A^2$-Nets: Double Attention Networks
Authors: Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng
Date: 2018-10-27 00:00:00
Tasks: 3D Absolute Human Pose Estimation, Action Classification, Action Recognition, Temporal Action Localization, Video Recognition

Learning to capture long-range relations is fundamental to image/video
recognition. Existing CNN models generally rely on increasing depth to model
such relations which is highly inefficient. In this work, we propose the
"double attention block", a novel component that aggregates and propagates
informative global features from the entire spatio-temporal space of input
images/videos, enabling subsequent convolution layers to access features from
the entire space efficiently. The component is designed with a double attention
mechanism in two steps, where the first step gathers features from the entire
space into a compact set through second-order attention pooling and the second
step adaptively selects and distributes features to each location via another
attention. The proposed double attention block is easy to adopt and can be
plugged into existing deep neural networks conveniently. We conduct extensive
ablation studies and experiments on both image and video recognition tasks for
evaluating its performance. On the image recognition task, a ResNet-50 equipped
with our double attention blocks outperforms a much larger ResNet-152
architecture on ImageNet-1k dataset with over 40% less the number of parameters
and less FLOPs. On the action recognition task, our proposed model achieves the
state-of-the-art results on the Kinetics and UCF-101 datasets with
significantly higher efficiency than recent works.
Short-segment heart sound classification using an ensemble of deep convolutional neural networks
Authors: Fuad Noman, Chee-Ming Ting, Sh-Hussain Salleh, Hernando Ombao
Date: 2018-10-27 00:00:00
Tasks: General Classification, Sound Classification, Specificity

This paper proposes a framework based on deep convolutional neural networks
(CNNs) for automatic heart sound classification using short-segments of
individual heart beats. We design a 1D-CNN that directly learns features from
raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional
time-frequency feature maps based on Mel-frequency cepstral coefficients
(MFCC). We further develop a time-frequency CNN ensemble (TF-ECNN) combining
the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities.
On the large PhysioNet CinC challenge 2016 database, the proposed CNN models
outperformed traditional classifiers based on support vector machine and hidden
Markov models with various hand-crafted time- and frequency-domain features.
Best classification scores with 89.22% accuracy and 89.94% sensitivity were
achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by
the 2D-CNN alone on the test set.