pwc-archive/papers-with-abstracts huggingface dataset:
Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images
Authors: Subhankar Roy, Enver Sangineto, Begüm Demir, Nicu Sebe
Date: 2019-04-02 00:00:00
Tasks: Computational Efficiency, Deep Hashing, Metric Learning, Retrieval

Hashing methods have been recently found very effective in retrieval of remote sensing (RS) images due to their computational efficiency and fast search speed. The traditional hashing methods in RS usually exploit hand-crafted features to learn hash functions to obtain binary codes, which can be insufficient to optimally represent the information content of RS images. To overcome this problem, in this paper we introduce a metric-learning based hashing network, which learns: 1) a semantic-based metric space for effective feature representation; and 2) compact binary hash codes for fast archive search. Our network considers an interplay of multiple loss functions that allows to jointly learn a metric based semantic space facilitating similar images to be clustered together in that target space and at the same time producing compact final activations that lose negligible information when binarized. Experiments carried out on two benchmark RS archives point out that the proposed network significantly improves the retrieval performance under the same retrieval time when compared to the state-of-the-art hashing methods in RS.
UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering
Authors: Zi-Yuan Chen, Chih-Hung Chang, Yi-Pei Chen, Jijnasa Nayak, Lun-Wei Ku
Date: 2019-04-02 00:00:00
Proceeding: NAACL 2019 6
Tasks: Question Answering, Relation, Relation Extraction

In relation extraction for knowledge-based question answering, searching from
one entity to another entity via a single relation is called "one hop". In
related work, an exhaustive search from all one-hop relations, two-hop
relations, and so on to the max-hop relations in the knowledge graph is
necessary but expensive. Therefore, the number of hops is generally restricted
to two or three. In this paper, we propose UHop, an unrestricted-hop framework
which relaxes this restriction by use of a transition-based search framework to
replace the relation-chain-based search one. We conduct experiments on
conventional 1- and 2-hop questions as well as lengthy questions, including
datasets such as WebQSP, PathQuestion, and Grid World. Results show that the
proposed framework enables the ability to halt, works well with
state-of-the-art models, achieves competitive performance without exhaustive
searches, and opens the performance gap for long relation paths.
Centerline Depth World Reinforcement Learning-based Left Atrial Appendage Orifice Localization
Authors: Walid Abdullah Al, Il Dong Yun, Eun Ju Chun
Date: 2019-04-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Left atrial appendage (LAA) closure (LAAC) is a minimally invasive implant-based method to prevent cardiovascular stroke in patients with non-valvular atrial fibrillation. Assessing the LAA orifice in preoperative CT angiography plays a crucial role in choosing an appropriate LAAC implant size and a proper C-arm angulation. However, accurate orifice localization is hard because of the high anatomic variation of LAA, and unclear position and orientation of the orifice in available CT views. Deep localization models also yield high error in localizing the orifice in CT image because of the tiny structure of orifice compared to the vastness of CT image. In this paper, we propose a centerline depth-based reinforcement learning (RL) world for effective orifice localization in a small search space. In our scheme, an RL agent observes the centerline-to-surface distance and navigates through the LAA centerline to localize the orifice. Thus, the search space is significantly reduced facilitating improved localization. The proposed formulation could result in high localization accuracy comparing to the expert-annotations in 98 CT images. Moreover, the localization process takes about 8 seconds which is 18 times more efficient than the existing method. Therefore, this can be a useful aid to physicians during the preprocedural planning of LAAC.
Adversarial Attacks against Deep Saliency Models
Authors: Zhaohui Che, Ali Borji, Guangtao Zhai, Suiyi Ling, Guodong Guo, Patrick Le Callet
Date: 2019-04-02 00:00:00
Tasks: Adversarial Attack, object-detection, Object Detection

Currently, a plethora of saliency models based on deep neural networks have
led great breakthroughs in many complex high-level vision tasks (e.g. scene
description, object detection). The robustness of these models, however, has
not yet been studied. In this paper, we propose a sparse feature-space
adversarial attack method against deep saliency models for the first time. The
proposed attack only requires a part of the model information, and is able to
generate a sparser and more insidious adversarial perturbation, compared to
traditional image-space attacks. These adversarial perturbations are so subtle
that a human observer cannot notice their presences, but the model outputs will
be revolutionized. This phenomenon raises security threats to deep saliency
models in practical applications. We also explore some intriguing properties of
the feature-space attack, e.g. 1) the hidden layers with bigger receptive
fields generate sparser perturbations, 2) the deeper hidden layers achieve
higher attack success rates, and 3) different loss functions and different
attacked layers will result in diverse perturbations. Experiments indicate that
the proposed method is able to successfully attack different model
architectures across various image scenes.
Deep Learning for Face Recognition: Pride or Prejudiced?
Authors: Shruti Nagpal, Maneet Singh, Richa Singh, Mayank Vatsa
Date: 2019-04-02 00:00:00
Tasks: Deep Learning, Face Recognition

Do very high accuracies of deep networks suggest pride of effective AI or are deep networks prejudiced? Do they suffer from in-group biases (own-race-bias and own-age-bias), and mimic the human behavior? Is in-group specific information being encoded sub-consciously by the deep networks? This research attempts to answer these questions and presents an in-depth analysis of `bias' in deep learning based face recognition systems. This is the first work which decodes if and where bias is encoded for face recognition. Taking cues from cognitive studies, we inspect if deep networks are also affected by social in- and out-group effect. Networks are analyzed for own-race and own-age bias, both of which have been well established in human beings. The sub-conscious behavior of face recognition models is examined to understand if they encode race or age specific features for face recognition. Analysis is performed based on 36 experiments conducted on multiple datasets. Four deep learning networks either trained from scratch or pre-trained on over 10M images are used. Variations across class activation maps and feature visualizations provide novel insights into the functioning of deep learning systems, suggesting behavior similar to humans. It is our belief that a better understanding of state-of-the-art deep learning networks would enable researchers to address the given challenge of bias in AI, and develop fairer systems.
DSAL-GAN: Denoising based Saliency Prediction with Generative Adversarial Networks
Authors: Prerana Mukherjee, Manoj Sharma, Megh Makwana, Ajay Pratap Singh, Avinash Upadhyay, Akkshita Trivedi, Brejesh lall, Santanu Chaudhury
Date: 2019-04-02 00:00:00
Tasks: Denoising, Generative Adversarial Network, object-detection, Object Detection, Prediction, RGB Salient Object Detection, Saliency Detection, Saliency Prediction, Salient Object Detection

Synthesizing high quality saliency maps from noisy images is a challenging
problem in computer vision and has many practical applications. Samples
generated by existing techniques for saliency detection cannot handle the noise
perturbations smoothly and fail to delineate the salient objects present in the
given scene. In this paper, we present a novel end-to-end coupled Denoising
based Saliency Prediction with Generative Adversarial Network (DSAL-GAN)
framework to address the problem of salient object detection in noisy images.
DSAL-GAN consists of two generative adversarial-networks (GAN) trained
end-to-end to perform denoising and saliency prediction altogether in a
holistic manner. The first GAN consists of a generator which denoises the noisy
input image, and in the discriminator counterpart we check whether the output
is a denoised image or ground truth original image. The second GAN predicts the
saliency maps from raw pixels of the input denoised image using a data-driven
metric based on saliency prediction method with adversarial loss. Cycle
consistency loss is also incorporated to further improve salient region
prediction. We demonstrate with comprehensive evaluation that the proposed
framework outperforms several baseline saliency models on various performance
benchmarks.
Enhancement of Energy-Based Swing-Up Controller via Entropy Search
Authors: Chang Sik Lee, Dong Eui Chang
Date: 2019-04-02 00:00:00
Tasks: Bayesian Optimization

An energy based approach for stabilizing a mechanical system has offered a
simple yet powerful control scheme. However, since it does not impose such
strong constraints on parameter space of the controller, finding appropriate
parameter values for an optimal controller is known to be hard. This paper
intends to generate an optimal energy-based controller for swinging up a rotary
inverted pendulum, also known as the Furuta pendulum, by applying the Bayesian
optimization called Entropy Search. Simulations and experiments show that the
optimal controller has an improved performance compared to a nominal controller
for various initial conditions.
Fence GAN: Towards Better Anomaly Detection
Authors: Cuong Phuc Ngo, Amadeus Aristo Winarto, Connie Kou Khor Li, Sojeong Park, Farhan Akram, Hwee Kuan Lee
Date: 2019-04-02 00:00:00
Tasks: Anomaly Classification, Anomaly Detection, Generative Adversarial Network

Anomaly detection is a classical problem where the aim is to detect anomalous
data that do not belong to the normal data distribution. Current
state-of-the-art methods for anomaly detection on complex high-dimensional data
are based on the generative adversarial network (GAN). However, the traditional
GAN loss is not directly aligned with the anomaly detection objective: it
encourages the distribution of the generated samples to overlap with the real
data and so the resulting discriminator has been found to be ineffective as an
anomaly detector. In this paper, we propose simple modifications to the GAN
loss such that the generated samples lie at the boundary of the real data
distribution. With our modified GAN loss, our anomaly detection method, called
Fence GAN (FGAN), directly uses the discriminator score as an anomaly
threshold. Our experimental results using the MNIST, CIFAR10 and KDD99 datasets
show that Fence GAN yields the best anomaly classification accuracy compared to
state-of-the-art methods.
Progressive LiDAR Adaptation for Road Detection
Authors: Zhe Chen, Jing Zhang, DaCheng Tao
Date: 2019-04-02 00:00:00

Despite rapid developments in visual image-based road detection, robustly
identifying road areas in visual images remains challenging due to issues like
illumination changes and blurry images. To this end, LiDAR sensor data can be
incorporated to improve the visual image-based road detection, because LiDAR
data is less susceptible to visual noises. However, the main difficulty in
introducing LiDAR information into visual image-based road detection is that
LiDAR data and its extracted features do not share the same space with the
visual data and visual features. Such gaps in spaces may limit the benefits of
LiDAR information for road detection. To overcome this issue, we introduce a
novel Progressive LiDAR Adaptation-aided Road Detection (PLARD) approach to
adapt LiDAR information into visual image-based road detection and improve
detection performance. In PLARD, progressive LiDAR adaptation consists of two
subsequent modules: 1) data space adaptation, which transforms the LiDAR data
to the visual data space to align with the perspective view by applying
altitude difference-based transformation; and 2) feature space adaptation,
which adapts LiDAR features to visual features through a cascaded fusion
structure. Comprehensive empirical studies on the well-known KITTI road
detection benchmark demonstrate that PLARD takes advantage of both the visual
and LiDAR information, achieving much more robust road detection even in
challenging urban scenes. In particular, PLARD outperforms other
state-of-the-art road detection models and is currently top of the publicly
accessible benchmark leader-board.
Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning
Authors: Mike Li, X. Rosalind Wang
Date: 2019-04-02 00:00:00
Tasks: Deep Learning

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of various complexities and analysed the alignment results quantitatively. We show the model has very good performance (AUC $\sim 1$ for simple data sets and AUC $\sim 0.85$ for very complex data sets). Further, our model easily outperforms existing algorithms on complex data sets. Compared with existing methods, ChromAlignNet is very easy to use as it requires no user input of reference chromatograms and parameters. This method can easily be adapted to other similar data such as those from liquid chromatography. The source code is written in Python and available online.
Habitat: A Platform for Embodied AI Research
Authors: Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, Dhruv Batra
Date: 2019-04-02 00:00:00
Proceeding: ICCV 2019 10
Tasks: Benchmarking, GPU, Instruction Following, PointGoal Navigation, Question Answering, Robot Navigation

We present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation. Specifically, Habitat consists of: (i) Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, sensors, and generic 3D dataset handling. Habitat-Sim is fast -- when rendering a scene from Matterport3D, it achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. (ii) Habitat-API: a modular high-level library for end-to-end development of embodied AI algorithms -- defining tasks (e.g., navigation, instruction following, question answering), configuring, training, and benchmarking embodied agents. These large-scale engineering contributions enable us to answer scientific questions requiring experiments that were till now impracticable or 'merely' impractical. Specifically, in the context of point-goal navigation: (1) we revisit the comparison between learning and SLAM approaches from two recent works and find evidence for the opposite conclusion -- that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and (2) we conduct the first cross-dataset generalization experiments {train, test} x {Matterport3D, Gibson} for multiple sensors {blind, RGB, RGBD, D} and find that only agents with depth (D) sensors generalize across datasets. We hope that our open-source platform and these findings will advance research in embodied AI.
Personalized Cancer Chemotherapy Schedule: a numerical comparison of performance and robustness in model-based and model-free scheduling methodologies
Authors: Jesus Tordesillas, Juncal Arbelaiz
Date: 2019-04-02 00:00:00
Tasks: Deep Reinforcement Learning, model, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Reinforcement learning algorithms are gaining popularity in fields in which optimal scheduling is important, and oncology is not an exception. The complex and uncertain dynamics of cancer limit the performance of traditional model-based scheduling strategies like Optimal Control. Motivated by the recent success of model-free Deep Reinforcement Learning (DRL) in challenging control tasks and in the design of medical treatments, we use Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) to design a personalized cancer chemotherapy schedule. We show that both of them succeed in the task and outperform the Optimal Control solution in the presence of uncertainty. Furthermore, we show that DDPG can exterminate cancer more efficiently than DQN presumably due to its continuous action space. Finally, we provide some insight regarding the amount of samples required for the training.
C2AE: Class Conditioned Auto-Encoder for Open-set Recognition
Authors: Poojan Oza, Vishal M. Patel
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Classification, Decoder, General Classification, image-classification, Image Classification, Open Set Learning

Models trained for classification often assume that all testing classes are
known while training. As a result, when presented with an unknown class during
testing, such closed-set assumption forces the model to classify it as one of
the known classes. However, in a real world scenario, classification models are
likely to encounter such examples. Hence, identifying those examples as unknown
becomes critical to model performance. A potential solution to overcome this
problem lies in a class of learning problems known as open-set recognition. It
refers to the problem of identifying the unknown classes during testing, while
maintaining performance on the known classes. In this paper, we propose an
open-set recognition algorithm using class conditioned auto-encoders with novel
training and testing methodology. In contrast to previous methods, training
procedure is divided in two sub-tasks, 1. closed-set classification and, 2.
open-set identification (i.e. identifying a class as known or unknown). Encoder
learns the first task following the closed-set classification training
pipeline, whereas decoder learns the second task by reconstructing conditioned
on class identity. Furthermore, we model reconstruction errors using the
Extreme Value Theory of statistical modeling to find the threshold for
identifying known/unknown class samples. Experiments performed on multiple
image classification datasets show proposed method performs significantly
better than state of the art.
Planning with Expectation Models
Authors: Yi Wan, Zaheer Abbas, Adam White, Martha White, Richard S. Sutton
Date: 2019-04-02 00:00:00
Tasks: Model-based Reinforcement Learning, Reinforcement Learning

Distribution and sample models are two popular model choices in model-based reinforcement learning (MBRL). However, learning these models can be intractable, particularly when the state and action spaces are large. Expectation models, on the other hand, are relatively easier to learn due to their compactness and have also been widely used for deterministic environments. For stochastic environments, it is not obvious how expectation models can be used for planning as they only partially characterize a distribution. In this paper, we propose a sound way of using approximate expectation models for MBRL. In particular, we 1) show that planning with an expectation model is equivalent to planning with a distribution model if the state value function is linear in state features, 2) analyze two common parametrization choices for approximating the expectation: linear and non-linear expectation models, 3) propose a sound model-based policy evaluation algorithm and present its convergence results, and 4) empirically demonstrate the effectiveness of the proposed planning algorithm.
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
Authors: Pengfei Zhang, Cuiling Lan, Wen-Jun Zeng, Junliang Xing, Jianru Xue, Nanning Zheng
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2020 6
Tasks: Action Recognition, Computational Efficiency, Skeleton Based Action Recognition, Temporal Action Localization

Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data. Recently, there is a trend of using very deep feedforward neural networks to model the 3D coordinates of joints without considering the computational efficiency. In this paper, we propose a simple yet effective semantics-guided neural network (SGN) for skeleton-based action recognition. We explicitly introduce the high level semantics of joints (joint type and frame index) into the network to enhance the feature representation capability. In addition, we exploit the relationship of joints hierarchically through two modules, i.e., a joint-level module for modeling the correlations of joints in the same frame and a framelevel module for modeling the dependencies of frames by taking the joints in the same frame as a whole. A strong baseline is proposed to facilitate the study of this field. With an order of magnitude smaller model size than most previous works, SGN achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets. The source code is available at https://github.com/microsoft/SGN.
Data-Free Learning of Student Networks
Authors: Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, Qi Tian
Date: 2019-04-02 00:00:00
Proceeding: ICCV 2019 10
Tasks: Neural Network Compression

Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors. Most existing deep neural network compression and speed-up methods are very effective for training compact deep models, when we can directly access the training dataset. However, training data for the given deep network are often unavailable due to some practice problems (e.g. privacy, legal issue, and transmission), and the architecture of the given network are also unknown except some interfaces. To this end, we propose a novel framework for training efficient deep neural networks by exploiting generative adversarial networks (GANs). To be specific, the pre-trained teacher networks are regarded as a fixed discriminator and the generator is utilized for derivating training samples which can obtain the maximum response on the discriminator. Then, an efficient network with smaller model size and computational complexity is trained using the generated data and the teacher network, simultaneously. Efficient student networks learned using the proposed Data-Free Learning (DAFL) method achieve 92.22% and 74.47% accuracies using ResNet-18 without any training data on the CIFAR-10 and CIFAR-100 datasets, respectively. Meanwhile, our student network obtains an 80.56% accuracy on the CelebA benchmark.