pwc-archive/papers-with-abstracts huggingface dataset:
Optimising Trotter-Suzuki Decompositions for Quantum Simulation Using Evolutionary Strategies
Authors: Benjamin D. M. Jones, George O. O'Brien, David R. White, Earl T. Campbell, John A. Clark
Date: 2019-04-02 00:00:00

One of the most promising applications of near-term quantum computing is the
simulation of quantum systems, a classically intractable task. Quantum
simulation requires computationally expensive matrix exponentiation;
Trotter-Suzuki decomposition of this exponentiation enables efficient
simulation to a desired accuracy on a quantum computer. We apply the Covariance
Matrix Adaptation Evolutionary Strategy (CMA-ES) algorithm to optimise the
Trotter-Suzuki decompositions of a canonical quantum system, the Heisenberg
Chain; we reduce simulation error by around 60%. We introduce this problem to
the computational search community, show that an evolutionary optimisation
approach is robust across runs and problem instances, and find that
optimisation results generalise to the simulation of larger systems.
Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks
Authors: Konstantin Posch, Jürgen Pilz
Date: 2019-04-02 00:00:00
Tasks: Variational Inference

In this article a novel approach for training deep neural networks using
Bayesian techniques is presented. The Bayesian methodology allows for an easy
evaluation of model uncertainty and additionally is robust to overfitting.
These are commonly the two main problems classical, i.e. non-Bayesian,
architectures have to struggle with. The proposed approach applies variational
inference in order to approximate the intractable posterior distribution. In
particular, the variational distribution is defined as product of multiple
multivariate normal distributions with tridiagonal covariance matrices. Each
single normal distribution belongs either to the weights, or to the biases
corresponding to one network layer. The layer-wise a posteriori variances are
defined based on the corresponding expectation values and further the
correlations are assumed to be identical. Therefore, only a few additional
parameters need to be optimized compared to non-Bayesian settings. The novel
approach is successfully evaluated on basis of the popular benchmark datasets
MNIST and CIFAR-10.
Point in, Box out: Beyond Counting Persons in Crowds
Authors: Yuting Liu, Miaojing Shi, Qijun Zhao, Xiaofang Wang
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Crowd Counting, regression

Modern crowd counting methods usually employ deep neural networks (DNN) to
estimate crowd counts via density regression. Despite their significant
improvements, the regression-based methods are incapable of providing the
detection of individuals in crowds. The detection-based methods, on the other
hand, have not been largely explored in recent trends of crowd counting due to
the needs for expensive bounding box annotations. In this work, we instead
propose a new deep detection network with only point supervision required. It
can simultaneously detect the size and location of human heads and count them
in crowds. We first mine useful person size information from point-level
annotations and initialize the pseudo ground truth bounding boxes. An online
updating scheme is introduced to refine the pseudo ground truth during
training; while a locally-constrained regression loss is designed to provide
additional constraints on the size of the predicted boxes in a local
neighborhood. In the end, we propose a curriculum learning strategy to train
the network from images of relatively accurate and easy pseudo ground truth
first. Extensive experiments are conducted in both detection and counting tasks
on several standard benchmarks, e.g. ShanghaiTech, UCF_CC_50, WiderFace, and
TRANCOS datasets, and the results show the superiority of our method over the
state-of-the-art.
HoloGAN: Unsupervised learning of 3D representations from natural images
Authors: Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, Yong-Liang Yang
Date: 2019-04-02 00:00:00
Proceeding: ICCV 2019 10
Tasks: Generative Adversarial Network, Image Generation, Novel View Synthesis

We propose a novel generative adversarial network (GAN) for the task of unsupervised learning of 3D representations from natural images. Most generative models rely on 2D kernels to generate images and make few assumptions about the 3D world. These models therefore tend to create blurry images or artefacts in tasks that require a strong 3D understanding, such as novel-view synthesis. HoloGAN instead learns a 3D representation of the world, and to render this representation in a realistic manner. Unlike other GANs, HoloGAN provides explicit control over the pose of generated objects through rigid-body transformations of the learnt 3D features. Our experiments show that using explicit 3D features enables HoloGAN to disentangle 3D pose and identity, which is further decomposed into shape and appearance, while still being able to generate images with similar or higher visual quality than other generative models. HoloGAN can be trained end-to-end from unlabelled 2D images only. Particularly, we do not require pose labels, 3D shapes, or multiple views of the same objects. This shows that HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner.
Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking
Authors: Saurabh Sharma, Pavan Teja Varigonda, Prashast Bindal, Abhishek Sharma, Arjun Jain
Date: 2019-04-02 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Human Pose Estimation, Image to 3D, Monocular 3D Human Pose Estimation, Multi-Hypotheses 3D Human Pose Estimation, Pose Estimation

Monocular 3D human-pose estimation from static images is a challenging problem, due to the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D. In this paper, we propose a Deep Conditional Variational Autoencoder based model that synthesizes diverse anatomically plausible 3D-pose samples conditioned on the estimated 2D-pose. We show that CVAE-based 3D-pose sample set is consistent with the 2D-pose and helps tackling the inherent ambiguity in 2D-to-3D lifting. We propose two strategies for obtaining the final 3D pose- (a) depth-ordering/ordinal relations to score and weight-average the candidate 3D-poses, referred to as OrdinalScore, and (b) with supervision from an Oracle. We report close to state of-the-art results on two benchmark datasets using OrdinalScore, and state-of-the-art results using the Oracle. We also show that our pipeline yields competitive results without paired image-to-3D annotations. The training and evaluation code is available at https://github.com/ssfootball04/generative_pose.
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents
Authors: Christian Rupprecht, Cyril Ibrahim, Christopher J. Pal
Date: 2019-04-02 00:00:00
Proceeding: ICLR 2020 1
Tasks: Autonomous Driving, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As deep reinforcement learning driven by visual perception becomes more
widely used there is a growing need to better understand and probe the learned
agents. Understanding the decision making process and its relationship to
visual inputs can be very valuable to identify problems in learned behavior.
However, this topic has been relatively under-explored in the research
community. In this work we present a method for synthesizing visual inputs of
interest for a trained agent. Such inputs or states could be situations in
which specific actions are necessary. Further, critical states in which a very
high or a very low reward can be achieved are often interesting to understand
the situational awareness of the system as they can correspond to risky states.
To this end, we learn a generative model over the state space of the
environment and use its latent space to optimize a target function for the
state of interest. In our experiments we show that this method can generate
insights for a variety of environments and reinforcement learning methods. We
explore results in the standard Atari benchmark games as well as in an
autonomous driving simulator. Based on the efficiency with which we have been
able to identify behavioural weaknesses with this technique, we believe this
general approach could serve as an important tool for AI safety applications.
Short Text Classification Improved by Feature Space Extension
Authors: Yanxuan Li
Date: 2019-04-02 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

With the explosive development of mobile Internet, short text has been
applied extensively. The difference between classifying short text and long
documents is that short text is of shortness and sparsity. Thus, it is
challenging to deal with short text classification owing to its less semantic
information. In this paper, we propose a novel topic-based convolutional neural
network (TB-CNN) based on Latent Dirichlet Allocation (LDA) model and
convolutional neural network. Comparing to traditional CNN methods, TB-CNN
generates topic words with LDA model to reduce the sparseness and combines the
embedding vectors of topic words and input words to extend feature space of
short text. The validation results on IMDB movie review dataset show the
improvement and effectiveness of TB-CNN.
DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis
Authors: Minfeng Zhu, Pingbo Pan, Wei Chen, Yi Yang
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Generative Adversarial Network, Image Generation, Text-to-Image Generation

In this paper, we focus on generating realistic images from text
descriptions. Current methods first generate an initial image with rough shape
and color, and then refine the initial image to a high-resolution one. Most
existing text-to-image synthesis methods have two main problems. (1) These
methods depend heavily on the quality of the initial images. If the initial
image is not well initialized, the following processes can hardly refine the
image to a satisfactory quality. (2) Each word contributes a different level of
importance when depicting different image contents, however, unchanged text
representation is used in existing image refinement processes. In this paper,
we propose the Dynamic Memory Generative Adversarial Network (DM-GAN) to
generate high-quality images. The proposed method introduces a dynamic memory
module to refine fuzzy image contents, when the initial images are not well
generated. A memory writing gate is designed to select the important text
information based on the initial image content, which enables our method to
accurately generate images from the text description. We also utilize a
response gate to adaptively fuse the information read from the memories and the
image features. We evaluate the DM-GAN model on the Caltech-UCSD Birds 200
dataset and the Microsoft Common Objects in Context dataset. Experimental
results demonstrate that our DM-GAN model performs favorably against the
state-of-the-art approaches.
CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification
Authors: Guillaume Delorme, Yihong Xu, Stephane Lathuilière, Radu Horaud, Xavier Alameda-Pineda
Date: 2019-04-02 00:00:00
Tasks: Clustering, Domain Adaptation, Person Re-Identification, Unsupervised Person Re-Identification

Unsupervised person re-ID is the task of identifying people on a target data set for which the ID labels are unavailable during training. In this paper, we propose to unify two trends in unsupervised person re-ID: clustering & fine-tuning and adversarial learning. On one side, clustering groups training images into pseudo-ID labels, and uses them to fine-tune the feature extractor. On the other side, adversarial learning is used, inspired by domain adaptation, to match distributions from different domains. Since target data is distributed across different camera viewpoints, we propose to model each camera as an independent domain, and aim to learn domain-independent features. Straightforward adversarial learning yields negative transfer, we thus introduce a conditioning vector to mitigate this undesirable effect. In our framework, the centroid of the cluster to which the visual sample belongs is used as conditioning vector of our conditional adversarial network, where the vector is permutation invariant (clusters ordering does not matter) and its size is independent of the number of clusters. To our knowledge, we are the first to propose the use of conditional adversarial networks for unsupervised person re-ID. We evaluate the proposed architecture on top of two state-of-the-art clustering-based unsupervised person re-identification (re-ID) methods on four different experimental settings with three different data sets and set the new state-of-the-art performance on all four of them. Our code and model will be made publicly available at https://team.inria.fr/perception/canu-reid/.
Pragmatically Informative Text Generation
Authors: Sheng Shen, Daniel Fried, Jacob Andreas, Dan Klein
Date: 2019-04-02 00:00:00
Proceeding: NAACL 2019 6
Tasks: Abstractive Text Summarization, Conditional Text Generation, Data-to-Text Generation, Grounded language learning, Informativeness, Text Generation

We improve the informativeness of models for conditional text generation
using techniques from computational pragmatics. These techniques formulate
language production as a game between speakers and listeners, in which a
speaker should generate output text that a listener can use to correctly
identify the original input that the text describes. While such approaches are
widely used in cognitive science and grounded language learning, they have
received less attention for more standard language generation tasks. We
consider two pragmatic modeling methods for text generation: one where
pragmatics is imposed by information preservation, and another where pragmatics
is imposed by explicit modeling of distractors. We find that these methods
improve the performance of strong existing systems for abstractive
summarization and generation from structured meaning representations.
Temporal and Aspectual Entailment
Authors: Thomas Kober, Sander Bijl de Vroe, Mark Steedman
Date: 2019-04-02 00:00:00
Proceeding: WS 2019 5
Tasks: Natural Language Inference

Inferences regarding "Jane's arrival in London" from predications such as
"Jane is going to London" or "Jane has gone to London" depend on tense and
aspect of the predications. Tense determines the temporal location of the
predication in the past, present or future of the time of utterance. The
aspectual auxiliaries on the other hand specify the internal constituency of
the event, i.e. whether the event of "going to London" is completed and whether
its consequences hold at that time or not. While tense and aspect are among the
most important factors for determining natural language inference, there has
been very little work to show whether modern NLP models capture these semantic
concepts. In this paper we propose a novel entailment dataset and analyse the
ability of a range of recently proposed NLP models to perform inference on
temporal predications. We show that the models encode a substantial amount of
morphosyntactic information relating to tense and aspect, but fail to model
inferences that require reasoning with these semantic properties.
Event-Based Motion Segmentation by Motion Compensation
Authors: Timo Stoffregen, Guillermo Gallego, Tom Drummond, Lindsay Kleeman, Davide Scaramuzza
Date: 2019-04-02 00:00:00
Proceeding: ICCV 2019 10
Tasks: Event Segmentation, Motion Compensation, Motion Segmentation, Segmentation

In contrast to traditional cameras, whose pixels have a common exposure time, event-based cameras are novel bio-inspired sensors whose pixels work independently and asynchronously output intensity changes (called "events"), with microsecond resolution. Since events are caused by the apparent motion of objects, event-based cameras sample visual information based on the scene dynamics and are, therefore, a more natural fit than traditional cameras to acquire motion, especially at high speeds, where traditional cameras suffer from motion blur. However, distinguishing between events caused by different moving objects and by the camera's ego-motion is a challenging task. We present the first per-event segmentation method for splitting a scene into independently moving objects. Our method jointly estimates the event-object associations (i.e., segmentation) and the motion parameters of the objects (or the background) by maximization of an objective function, which builds upon recent results on event-based motion-compensation. We provide a thorough evaluation of our method on a public dataset, outperforming the state-of-the-art by as much as 10%. We also show the first quantitative evaluation of a segmentation algorithm for event cameras, yielding around 90% accuracy at 4 pixels relative displacement.
FKIMNet: A Finger Dorsal Image Matching Network Comparing Component (Major, Minor and Nail) Matching with Holistic (Finger Dorsal) Matching
Authors: Daksh Thapar, Gaurav Jaswal, Aditya Nigam
Date: 2019-04-02 00:00:00
Tasks: Data Augmentation, Triplet

Current finger knuckle image recognition systems, often require users to
place fingers' major or minor joints flatly towards the capturing sensor. To
extend these systems for user non-intrusive application scenarios, such as
consumer electronics, forensic, defence etc, we suggest matching the full
dorsal fingers, rather than the major/ minor region of interest (ROI) alone. In
particular, this paper makes a comprehensive study on the comparisons between
full finger and fusion of finger ROI's for finger knuckle image recognition.
These experiments suggest that using full-finger, provides a more elegant
solution. Addressing the finger matching problem, we propose a CNN
(convolutional neural network) which creates a $128$-D feature embedding of an
image. It is trained via. triplet loss function, which enforces the L2 distance
between the embeddings of the same subject to be approaching zero, whereas the
distance between any 2 embeddings of different subjects to be at least a
margin. For precise training of the network, we use dynamic adaptive margin,
data augmentation, and hard negative mining. In distinguished experiments, the
individual performance of finger, as well as weighted sum score level fusion of
major knuckle, minor knuckle, and nail modalities have been computed,
justifying our assumption to consider full finger as biometrics instead of its
counterparts. The proposed method is evaluated using two publicly available
finger knuckle image datasets i.e., PolyU FKP dataset and PolyU Contactless FKI
Datasets.
A PCA-like Autoencoder
Authors: Saïd Ladjal, Alasdair Newson, Chi-Hieu Pham
Date: 2019-04-02 00:00:00

An autoencoder is a neural network which data projects to and from a lower
dimensional latent space, where this data is easier to understand and model.
The autoencoder consists of two sub-networks, the encoder and the decoder,
which carry out these transformations. The neural network is trained such that
the output is as close to the input as possible, the data having gone through
an information bottleneck : the latent space. This tool bears significant
ressemblance to Principal Component Analysis (PCA), with two main differences.
Firstly, the autoencoder is a non-linear transformation, contrary to PCA, which
makes the autoencoder more flexible and powerful. Secondly, the axes found by a
PCA are orthogonal, and are ordered in terms of the amount of variability which
the data presents along these axes. This makes the interpretability of the PCA
much greater than that of the autoencoder, which does not have these
attributes. Ideally, then, we would like an autoencoder whose latent space
consists of independent components, ordered by decreasing importance to the
data. In this paper, we propose an algorithm to create such a network. We
create an iterative algorithm which progressively increases the size of the
latent space, learning a new dimension at each step. Secondly, we propose a
covariance loss term to add to the standard autoencoder loss function, as well
as a normalisation layer just before the latent space, which encourages the
latent space components to be statistically independent. We demonstrate the
results of this autoencoder on simple geometric shapes, and find that the
algorithm indeed finds a meaningful representation in the latent space. This
means that subsequent interpolation in the latent space has meaning with
respect to the geometric properties of the images.
Experiments on Open-Set Speaker Identification with Discriminatively Trained Neural Networks
Authors: Stefano Imoscopi, Volodya Grancharov, Sigurdur Sverrisson, Erlendur Karlsson, Harald Pobloth
Date: 2019-04-02 00:00:00
Tasks: Speaker Identification

This paper presents a study on discriminative artificial neural network
classifiers in the context of open-set speaker identification. Both 2-class and
multi-class architectures are tested against the conventional Gaussian mixture
model based classifier on enrolled speaker sets of different sizes. The
performance evaluation shows that the multi-class neural network system has
superior performance for large population sizes.
Hierarchical method for cataract grading based on retinal images using improved Haar wavelet
Authors: Lvchen Cao, Huiqi Li, Yanjun Zhang, Liang Xu, Li Zhang
Date: 2019-04-02 00:00:00
Tasks: Classification, General Classification

Cataracts, which are lenticular opacities that may occur at different lens
locations, are the leading cause of visual impairment worldwide. Accurate and
timely diagnosis can improve the quality of life of cataract patients. In this
paper, a feature extraction-based method for grading cataract severity using
retinal images is proposed. To obtain more appropriate features for the
automatic grading, the Haar wavelet is improved according to the
characteristics of retinal images. Retinal images of non-cataract, as well as
mild, moderate, and severe cataracts, are automatically recognized using the
improved Haar wavelet. A hierarchical strategy is used to transform the
four-class classification problem into three adjacent two-class classification
problems. Three sets of two-class classifiers based on a neural network are
trained individually and integrated together to establish a complete
classification system. The accuracies of the two-class classification (cataract
and non-cataract) and four-class classification are 94.83% and 85.98%,
respectively. The performance analysis demonstrates that the improved Haar
wavelet feature achieves higher accuracy than the original Haar wavelet
feature, and the fusion of three sets of two-class classifiers is superior to a
simple four-class classifier. The discussion indicates that the retinal
image-based method offers significant potential for cataract detection.