pwc-archive/papers-with-abstracts huggingface dataset:
Photometric Depth Super-Resolution
Authors: Bjoern Haefner, Songyou Peng, Alok Verma, Yvain Quéau, Daniel Cremers
Date: 2018-09-26 00:00:00
Tasks: Super-Resolution

This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to the higher resolution of the companion RGB image. A single-shot variational approach is first put forward, which is effective as long as the target's reflectance is piecewise-constant. It is then shown that this dependency upon a specific reflectance model can be relaxed by focusing on a specific class of objects (e.g., faces), and delegate reflectance estimation to a deep neural network. A multi-shot strategy based on randomly varying lighting conditions is eventually discussed. It requires no training or prior on the reflectance, yet this comes at the price of a dedicated acquisition setup. Both quantitative and qualitative evaluations illustrate the effectiveness of the proposed methods on synthetic and real-world scenarios.
Pay attention! - Robustifying a Deep Visuomotor Policy through Task-Focused Attention
Authors: Pooya Abolghasemi, Amir Mazaheri, Mubarak Shah, Ladislau Bölöni
Date: 2018-09-26 00:00:00

Several recent studies have demonstrated the promise of deep visuomotor
policies for robot manipulator control. Despite impressive progress, these
systems are known to be vulnerable to physical disturbances, such as accidental
or adversarial bumps that make them drop the manipulated object. They also tend
to be distracted by visual disturbances such as objects moving in the robot's
field of view, even if the disturbance does not physically prevent the
execution of the task. In this paper, we propose an approach for augmenting a
deep visuomotor policy trained through demonstrations with Task Focused visual
Attention (TFA). The manipulation task is specified with a natural language
text such as `move the red bowl to the left'. This allows the visual attention
component to concentrate on the current object that the robot needs to
manipulate. We show that even in benign environments, the TFA allows the policy
to consistently outperform a variant with no attention mechanism. More
importantly, the new policy is significantly more robust: it regularly recovers
from severe physical disturbances (such as bumps causing it to drop the object)
from which the baseline policy, i.e. with no visual attention, almost never
recovers. In addition, we show that the proposed policy performs correctly in
the presence of a wide class of visual disturbances, exhibiting a behavior
reminiscent of human selective visual attention experiments. Our proposed
approach consists of a VAE-GAN network which encodes the visual input and feeds
it to a Motor network that moves the robot joints. Also, our approach benefits
from a teacher network for the TFA that leverages textual input command to
robustify the visual encoder against various types of disturbances.
Residuum-Condition Diagram and Reduction of Over-Complete Endmember-Sets
Authors: Christoph Schikora, Markus Plack, Andreas Kolb
Date: 2018-09-26 00:00:00

Extracting reference spectra, or endmembers (EMs) from a given multi- or
hyperspectral image, as well as estimating the size of the EM set, plays an
important role in multispectral image processing. In this paper, we present
condition-residuum-diagrams. By plotting the residuum resulting from the
unmixing and reconstruction and the condition number of various EM sets, the
resulting diagram provides insight into the behavior of the spectral unmixing
under a varying amount of endmembers (EMs). Furthermore, we utilize
condition-residuum-diagrams to realize an EM reduction algorithm that starts
with an initially extracted, over-complete EM set. An over-complete EM set
commonly exhibits a good unmixing result, i.e. a lower reconstruction residuum,
but due to its partial redundancy, the unmixing gets numerically unstable, i.e.
the unmixed abundances values are less reliable. Our greedy reduction scheme
improves the EM set by reducing the condition number, i.e. enhancing the set's
stability, while keeping the reconstruction error as low as possible. The
resulting set sequence gives hint to the optimal EM set and its size. We
demonstrate the benefit of our condition-residuum-diagram and reduction scheme
on well-studied datasets with known reference EM set sizes for several
well-known EE algorithms.
Unsupervised Adversarial Invariance
Authors: Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Premkumar Natarajan
Date: 2018-09-26 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Data Augmentation, Disentanglement, Domain Adaptation, General Classification, Multi-class Classification

Data representations that contain all the information about target variables
but are invariant to nuisance factors benefit supervised learning algorithms by
preventing them from learning associations between these factors and the
targets, thus reducing overfitting. We present a novel unsupervised invariance
induction framework for neural networks that learns a split representation of
data through competitive training between the prediction task and a
reconstruction task coupled with disentanglement, without needing any labeled
information about nuisance factors or domain knowledge. We describe an
adversarial instantiation of this framework and provide analysis of its
working. Our unsupervised model outperforms state-of-the-art methods, which are
supervised, at inducing invariance to inherent nuisance factors, effectively
using synthetic data augmentation to learn invariance, and domain adaptation.
Our method can be applied to any prediction task, eg., binary/multi-class
classification or regression, without loss of generality.
Rediscovering Deep Neural Networks Through Finite-State Distributions
Authors: Amir Emad Marvasti, Ehsan Emad Marvasti, George Atia, Hassan Foroosh
Date: 2018-09-26 00:00:00

We propose a new way of thinking about deep neural networks, in which the linear and non-linear components of the network are naturally derived and justified in terms of principles in probability theory. In particular, the models constructed in our framework assign probabilities to uncertain realizations, leading to Kullback-Leibler Divergence (KLD) as the linear layer. In our model construction, we also arrive at a structure similar to ReLU activation supported with Bayes' theorem. The non-linearities in our framework are normalization layers with ReLU and Sigmoid as element-wise approximations. Additionally, the pooling function is derived as a marginalization of spatial random variables according to the mechanics of the framework. As such, Max Pooling is an approximation to the aforementioned marginalization process. Since our models are comprised of finite state distributions (FSD) as variables and parameters, exact computation of information-theoretic quantities such as entropy and KLD is possible, thereby providing more objective measures to analyze networks. Unlike existing designs that rely on heuristics, the proposed framework restricts subjective interpretations of CNNs and sheds light on the functionality of neural networks from a completely new perspective.
General-purpose Declarative Inductive Programming with Domain-Specific Background Knowledge for Data Wrangling Automation
Authors: Lidia Contreras-Ochando, César Ferri, José Hernández-Orallo, Fernando Martínez-Plumed, María José Ramírez-Quintana, Susumu Katayama
Date: 2018-09-26 00:00:00

Given one or two examples, humans are good at understanding how to solve a
problem independently of its domain, because they are able to detect what the
problem is and to choose the appropriate background knowledge according to the
context. For instance, presented with the string "8/17/2017" to be transformed
to "17th of August of 2017", humans will process this in two steps: (1) they
recognise that it is a date and (2) they map the date to the 17th of August of
2017. Inductive Programming (IP) aims at learning declarative (functional or
logic) programs from examples. Two key advantages of IP are the use of
background knowledge and the ability to synthesise programs from a few
input/output examples (as humans do). In this paper we propose to use IP as a
means for automating repetitive data manipulation tasks, frequently presented
during the process of {\em data wrangling} in many data manipulation problems.
Here we show that with the use of general-purpose declarative (programming)
languages jointly with generic IP systems and the definition of domain-specific
knowledge, many specific data wrangling problems from different application
domains can be automatically solved from very few examples. We also propose an
integrated benchmark for data wrangling, which we share publicly for the
community.
Sampling Theory for Graph Signals on Product Graphs
Authors: Rohan Varma, Jelena Kovačević
Date: 2018-09-26 00:00:00

In this paper, we extend the sampling theory on graphs by constructing a
framework that exploits the structure in product graphs for efficient sampling
and recovery of bandlimited graph signals that lie on them. Product graphs are
graphs that are composed from smaller graph atoms; we motivate how this model
is a flexible and useful way to model richer classes of data that can be
multi-modal in nature. Previous works have established a sampling theory on
graphs for bandlimited signals. Importantly, the framework achieves significant
savings in both sample complexity and computational complexity
No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph
Authors: Kuldeep Singh, Ioanna Lytra, Arun Sethupat Radhakrishna, Saeedeh Shekarpour, Maria-Esther Vidal, Jens Lehmann
Date: 2018-09-26 00:00:00
Tasks: Knowledge Graphs, Question Answering

Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction. DBpedia has been the most prominently used knowledge graph in this setting and most approaches currently use a pipeline of processing steps connecting a sequence of components. In this article, we analyse and micro evaluate the behaviour of 29 available QA components for DBpedia knowledge graph that were released by the research community since 2010. As a result, we provide a perspective on collective failure cases, suggest characteristics of QA components that prevent them from performing better and provide future challenges and research directions for the field.
Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis
Authors: Kelly W. Zhang, Samuel R. Bowman
Date: 2018-09-26 00:00:00
Tasks: Language Modeling, Language Modelling, Transfer Learning, Translation

Recent work using auxiliary prediction task classifiers to investigate the
properties of LSTM representations has begun to shed light on why pretrained
representations, like ELMo (Peters et al., 2018) and CoVe (McCann et al.,
2017), are so beneficial for neural language understanding models. We still,
though, do not yet have a clear understanding of how the choice of pretraining
objective affects the type of linguistic information that models learn. With
this in mind, we compare four objectives---language modeling, translation,
skip-thought, and autoencoding---on their ability to induce syntactic and
part-of-speech information. We make a fair comparison between the tasks by
holding constant the quantity and genre of the training data, as well as the
LSTM architecture. We find that representations from language models
consistently perform best on our syntactic auxiliary prediction tasks, even
when trained on relatively small amounts of data. These results suggest that
language modeling may be the best data-rich pretraining task for transfer
learning applications requiring syntactic information. We also find that the
representations from randomly-initialized, frozen LSTMs perform strikingly well
on our syntactic auxiliary tasks, but this effect disappears when the amount of
training data for the auxiliary tasks is reduced.
Using Neural Networks to Generate Information Maps for Mobile Sensors
Authors: Louis Dressel, Mykel J. Kochenderfer
Date: 2018-09-26 00:00:00

Target localization is a critical task for mobile sensors and has many
applications. However, generating informative trajectories for these sensors is
a challenging research problem. A common method uses information maps that
estimate the value of taking measurements from any point in the sensor state
space. These information maps are used to generate trajectories; for example, a
trajectory might be designed so its distribution of measurements matches the
distribution of the information map. Regardless of the trajectory generation
method, generating information maps as new observations are made is critical.
However, it can be challenging to compute these maps in real-time. We propose
using convolutional neural networks to generate information maps from a target
estimate and sensor model in real-time. Simulations show that maps are
accurately rendered while offering orders of magnitude reduction in computation
time.
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas
Authors: Nicolas Anastassacos, Mirco Musolesi
Date: 2018-09-26 00:00:00
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multi-agent reinforcement learning has received significant interest in
recent years notably due to the advancements made in deep reinforcement
learning which have allowed for the developments of new architectures and
learning algorithms. Using social dilemmas as the training ground, we present a
novel learning architecture, Learning through Probing (LTP), where agents
utilize a probing mechanism to incorporate how their opponent's behavior
changes when an agent takes an action. We use distinct training phases and
adjust rewards according to the overall outcome of the experiences accounting
for changes to the opponents behavior. We introduce a parameter eta to
determine the significance of these future changes to opponent behavior. When
applied to the Iterated Prisoner's Dilemma (IPD), LTP agents demonstrate that
they can learn to cooperate with each other, achieving higher average
cumulative rewards than other reinforcement learning methods while also
maintaining good performance in playing against static agents that are present
in Axelrod tournaments. We compare this method with traditional reinforcement
learning algorithms and agent-tracking techniques to highlight key differences
and potential applications. We also draw attention to the differences between
solving games and societal-like interactions and analyze the training of
Q-learning agents in makeshift societies. This is to emphasize how cooperation
may emerge in societies and demonstrate this using environments where
interactions with opponents are determined through a random encounter format of
the IPD.
A Novel Online Stacked Ensemble for Multi-Label Stream Classification
Authors: Alican Büyükçakır, Hamed Bonab, Fazli Can
Date: 2018-09-26 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

As data streams become more prevalent, the necessity for online algorithms
that mine this transient and dynamic data becomes clearer. Multi-label data
stream classification is a supervised learning problem where each instance in
the data stream is classified into one or more pre-defined sets of labels. Many
methods have been proposed to tackle this problem, including but not limited to
ensemble-based methods. Some of these ensemble-based methods are specifically
designed to work with certain multi-label base classifiers; some others employ
online bagging schemes to build their ensembles. In this study, we introduce a
novel online and dynamically-weighted stacked ensemble for multi-label
classification, called GOOWE-ML, that utilizes spatial modeling to assign
optimal weights to its component classifiers. Our model can be used with any
existing incremental multi-label classification algorithm as its base
classifier. We conduct experiments with 4 GOOWE-ML-based multi-label ensembles
and 7 baseline models on 7 real-world datasets from diverse areas of interest.
Our experiments show that GOOWE-ML ensembles yield consistently better results
in terms of predictive performance in almost all of the datasets, with respect
to the other prominent ensemble models.
Random Occlusion-recovery for Person Re-identification
Authors: Di Wu, Kun Zhang, Fei Cheng, Yang Zhao, Qi Liu, Chang-An Yuan, De-Shuang Huang
Date: 2018-09-26 00:00:00
Tasks: Generative Adversarial Network, Person Re-Identification

As a basic task of multi-camera surveillance system, person re-identification
aims to re-identify a query pedestrian observed from non-overlapping multiple
cameras or across different time with a single camera. Recently, deep
learning-based person re-identification models have achieved great success in
many benchmarks. However, these supervised models require a large amount of
labeled image data, and the process of manual labeling spends much manpower and
time. In this study, we introduce a method to automatically synthesize labeled
person images and adopt them to increase the sample number per identity for
person re-identification datasets. To be specific, we use block rectangles to
randomly occlude pedestrian images. Then, a generative adversarial network
(GAN) model is proposed to use paired occluded and original images to
synthesize the de-occluded images that similar but not identical to the
original image. Afterwards, we annotate the de-occluded images with the same
labels of their corresponding raw images and use them to augment the number of
samples per identity. Finally, we use the augmented datasets to train baseline
model. The experiment results on CUHK03, Market-1501 and DukeMTMC-reID datasets
show that the effectiveness of the proposed method.
Deep Neural Networks for Estimation and Inference
Authors: Max H. Farrell, Tengyuan Liang, Sanjog Misra
Date: 2018-09-26 00:00:00
Tasks: Marketing, regression, valid

We study deep neural networks and their use in semiparametric inference. We establish novel rates of convergence for deep feedforward neural nets. Our new rates are sufficiently fast (in some cases minimax optimal) to allow us to establish valid second-step inference after first-step estimation with deep learning, a result also new to the literature. Our estimation rates and semiparametric inference results handle the current standard architecture: fully connected feedforward neural networks (multi-layer perceptrons), with the now-common rectified linear unit activation function and a depth explicitly diverging with the sample size. We discuss other architectures as well, including fixed-width, very deep networks. We establish nonasymptotic bounds for these deep nets for a general class of nonparametric regression-type loss functions, which includes as special cases least squares, logistic regression, and other generalized linear models. We then apply our theory to develop semiparametric inference, focusing on causal parameters for concreteness, such as treatment effects, expected welfare, and decomposition effects. Inference in many other semiparametric contexts can be readily obtained. We demonstrate the effectiveness of deep learning with a Monte Carlo analysis and an empirical application to direct mail marketing.
Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking
Authors: Yewei Huang, Junqiao Zhao, Xudong He, Shaoming Zhang, Tiantian Feng
Date: 2018-09-26 00:00:00
Tasks: Autonomous Driving, Indoor Localization

In this paper, we proposed a novel and practical solution for the real-time
indoor localization of autonomous driving in parking lots. High-level
landmarks, the parking slots, are extracted and enriched with labels to avoid
the aliasing of low-level visual features. We then proposed a robust method for
detecting incorrect data associations between parking slots and further
extended the optimization framework by dynamically eliminating suboptimal data
associations. Visual fiducial markers are introduced to improve the overall
precision. As a result, a semantic map of the parking lot can be established
fully automatically and robustly. We experimented the performance of real-time
localization based on the map using our autonomous driving platform TiEV, and
the average accuracy of 0.3m track tracing can be achieved at a speed of 10kph.
Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning
Authors: Yawei Luo, Tao Guan, Junqing Yu, Ping Liu, Yi Yang
Date: 2018-09-26 00:00:00
Tasks: General Classification, Node Classification

Graph convolutional network (GCN) provides a powerful means for graph-based
semi-supervised tasks. However, as a localized first-order approximation of
spectral graph convolution, the classic GCN can not take full advantage of
unlabeled data, especially when the unlabeled node is far from labeled ones. To
capitalize on the information from unlabeled nodes to boost the training for
GCN, we propose a novel framework named Self-Ensembling GCN (SEGCN), which
marries GCN with Mean Teacher - another powerful model in semi-supervised
learning. SEGCN contains a student model and a teacher model. As a student, it
not only learns to correctly classify the labeled nodes, but also tries to be
consistent with the teacher on unlabeled nodes in more challenging situations,
such as a high dropout rate and graph collapse. As a teacher, it averages the
student model weights and generates more accurate predictions to lead the
student. In such a mutual-promoting process, both labeled and unlabeled samples
can be fully utilized for backpropagating effective gradients to train GCN. In
three article classification tasks, i.e. Citeseer, Cora and Pubmed, we validate
that the proposed method matches the state of the arts in the classification
accuracy.
Hierarchy-based Image Embeddings for Semantic Image Retrieval
Authors: Björn Barz, Joachim Denzler
Date: 2018-09-26 00:00:00
Tasks: Few-Shot Learning, Image Retrieval, Novelty Detection, Retrieval, Semantic Similarity, Semantic Textual Similarity, World Knowledge

Deep neural networks trained for classification have been found to learn
powerful image representations, which are also often used for other tasks such
as comparing images w.r.t. their visual similarity. However, visual similarity
does not imply semantic similarity. In order to learn semantically
discriminative features, we propose to map images onto class embeddings whose
pair-wise dot products correspond to a measure of semantic similarity between
classes. Such an embedding does not only improve image retrieval results, but
could also facilitate integrating semantics for other tasks, e.g., novelty
detection or few-shot learning. We introduce a deterministic algorithm for
computing the class centroids directly based on prior world-knowledge encoded
in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds,
and ImageNet show that our learned semantic image embeddings improve the
semantic consistency of image retrieval results by a large margin.