pwc-archive/papers-with-abstracts huggingface dataset:
Human Semantic Parsing for Person Re-identification
Authors: Mahdi M. Kalayeh, Emrah Basaran, Muhittin Gokmen, Mustafa E. Kamasak, Mubarak Shah
Date: 2018-03-31 00:00:00
Proceeding: CVPR 2018 6
Tasks: Person Re-Identification, Representation Learning, Semantic Parsing

Person re-identification is a challenging task mainly due to factors such as
background clutter, pose, illumination and camera point of view variations.
These elements hinder the process of extracting robust and discriminative
representations, hence preventing different identities from being successfully
distinguished. To improve the representation learning, usually, local features
from human body parts are extracted. However, the common practice for such a
process has been based on bounding box part detection. In this paper, we
propose to adopt human semantic parsing which, due to its pixel-level accuracy
and capability of modeling arbitrary contours, is naturally a better
alternative. Our proposed SPReID integrates human semantic parsing in person
re-identification and not only considerably outperforms its counter baseline,
but achieves state-of-the-art performance. We also show that by employing a
\textit{simple} yet effective training strategy, standard popular deep
convolutional architectures such as Inception-V3 and ResNet-152, with no
modification, while operating solely on full image, can dramatically outperform
current state-of-the-art. Our proposed methods improve state-of-the-art person
re-identification on: Market-1501 by ~17% in mAP and ~6% in rank-1, CUHK03 by
~4% in rank-1 and DukeMTMC-reID by ~24% in mAP and ~10% in rank-1.
Gated Fusion Network for Single Image Dehazing
Authors: Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, Ming-Hsuan Yang
Date: 2018-03-31 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Image Dehazing, Single Image Dehazing

In this paper, we propose an efficient algorithm to directly restore a clear
image from a hazy input. The proposed algorithm hinges on an end-to-end
trainable neural network that consists of an encoder and a decoder. The encoder
is exploited to capture the context of the derived input images, while the
decoder is employed to estimate the contribution of each input to the final
dehazed result using the learned representations attributed to the encoder. The
constructed network adopts a novel fusion-based strategy which derives three
inputs from an original hazy image by applying White Balance (WB), Contrast
Enhancing (CE), and Gamma Correction (GC). We compute pixel-wise confidence
maps based on the appearance differences between these different inputs to
blend the information of the derived inputs and preserve the regions with
pleasant visibility. The final dehazed image is yielded by gating the important
features of the derived inputs. To train the network, we introduce a
multi-scale approach such that the halo artifacts can be avoided. Extensive
experimental results on both synthetic and real-world images demonstrate that
the proposed algorithm performs favorably against the state-of-the-art
algorithms.
Efficient Encodings of Conditional Cardinality Constraints
Authors: Abdelhamid Boudane, Said Jabbour, Badran Raddaoui, Lakhdar Sais
Date: 2018-03-31 00:00:00
Tasks: ARC

In the encoding of many real-world problems to propositional satisfiability,
the cardinality constraint is a recurrent constraint that needs to be managed
effectively. Several efficient encodings have been proposed while missing that
such a constraint can be involved in a more general propositional formulation.
To avoid combinatorial explosion, Tseitin principle usually used to translate
such general propositional formula to Conjunctive Normal Form (CNF), introduces
fresh propositional variables to represent sub-formulas and/or complex
contraints. Thanks to Plaisted and Greenbaum improvement, the polarity of the
sub-formula $\Phi$ is taken into account leading to conditional constraints of
the form $y\rightarrow \Phi$, or $\Phi\rightarrow y$, where $y$ is a fresh
propositional variable. In the case where $\Phi$ represents a cardinality
constraint, such translation leads to conditional cardinality constraints
subject of the present paper. We first show that when all the clauses encoding
the cardinality constraint are augmented with an additional new variable, most
of the well-known encodings cease to maintain the generalized arc consistency
property. Then, we consider some of these encodings and show how they can be
extended to recover such important property. An experimental validation is
conducted on a SAT-based pattern mining application, where such conditional
cardinality constraints is a cornerstone, showing the relevance of our proposed
approach.
In-depth Question classification using Convolutional Neural Networks
Authors: Prudhvi Raj Dachapally, Srikanth Ramanam
Date: 2018-03-31 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, text-classification, Text Classification

Convolutional neural networks for computer vision are fairly intuitive. In a
typical CNN used in image classification, the first layers learn edges, and the
following layers learn some filters that can identify an object. But CNNs for
Natural Language Processing are not used often and are not completely
intuitive. We have a good idea about what the convolution filters learn for the
task of text classification, and to that, we propose a neural network structure
that will be able to give good results in less time. We will be using
convolutional neural networks to predict the primary or broader topic of a
question, and then use separate networks for each of these predicted topics to
accurately classify their sub-topics.
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods
Authors: Robert M. Gower, Nicolas Le Roux, Francis Bach
Date: 2017-10-20 00:00:00

Our goal is to improve variance reducing stochastic methods through better
control variates. We first propose a modification of SVRG which uses the
Hessian to track gradients over time, rather than to recondition, increasing
the correlation of the control variates and leading to faster theoretical
convergence close to the optimum. We then propose accurate and computationally
efficient approximations to the Hessian, both using a diagonal and a low-rank
matrix. Finally, we demonstrate the effectiveness of our method on a wide range
of problems.
CSGNet: Neural Shape Parser for Constructive Solid Geometry
Authors: Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji
Date: 2017-12-22 00:00:00
Proceeding: CVPR 2018 6

We present a neural architecture that takes as input a 2D or 3D shape and
outputs a program that generates the shape. The instructions in our program are
based on constructive solid geometry principles, i.e., a set of boolean
operations on shape primitives defined recursively. Bottom-up techniques for
this shape parsing task rely on primitive detection and are inherently slow
since the search space over possible primitive combinations is large. In
contrast, our model uses a recurrent neural network that parses the input shape
in a top-down manner, which is significantly faster and yields a compact and
easy-to-interpret sequence of modeling instructions. Our model is also more
effective as a shape detector compared to existing state-of-the-art detection
techniques. We finally demonstrate that our network can be trained on novel
datasets without ground-truth program annotations through policy gradient
techniques.
Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning
Authors: Łukasz Kidziński, Sharada P. Mohanty, Carmichael Ong, Jennifer L. Hicks, Sean F. Carroll, Sergey Levine, Marcel Salathé, Scott L. Delp
Date: 2018-03-31 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Synthesizing physiologically-accurate human movement in a variety of
conditions can help practitioners plan surgeries, design experiments, or
prototype assistive devices in simulated environments, reducing time and costs
and improving treatment outcomes. Because of the large and complex solution
spaces of biomechanical models, current methods are constrained to specific
movements and models, requiring careful design of a controller and hindering
many possible applications. We sought to discover if modern optimization
methods efficiently explore these complex spaces. To do this, we posed the
problem as a competition in which participants were tasked with developing a
controller to enable a physiologically-based human model to navigate a complex
obstacle course as quickly as possible, without using any experimental data.
They were provided with a human musculoskeletal model and a physics-based
simulation environment. In this paper, we discuss the design of the
competition, technical difficulties, results, and analysis of the top
controllers. The challenge proved that deep reinforcement learning techniques,
despite their high computational cost, can be successfully employed as an
optimization method for synthesizing physiologically feasible motion in
high-dimensional biomechanical systems.
The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples
Authors: Ayse Elvan Aydemir, Alptekin Temizel, Tugba Taskaya Temizel
Date: 2018-03-28 00:00:00
Tasks: Blocking, Image Compression

Adversarial examples are known to have a negative effect on the performance
of classifiers which have otherwise good performance on undisturbed images.
These examples are generated by adding non-random noise to the testing samples
in order to make classifier misclassify the given data. Adversarial attacks use
these intentionally generated examples and they pose a security risk to the
machine learning based systems. To be immune to such attacks, it is desirable
to have a pre-processing mechanism which removes these effects causing
misclassification while keeping the content of the image. JPEG and JPEG2000 are
well-known image compression techniques which suppress the high-frequency
content taking the human visual system into account. JPEG has been also shown
to be an effective method for reducing adversarial noise. In this paper, we
propose applying JPEG2000 compression as an alternative and systematically
compare the classification performance of adversarial images compressed using
JPEG and JPEG2000 at different target PSNR values and maximum compression
levels. Our experiments show that JPEG2000 is more effective in reducing
adversarial noise as it allows higher compression rates with less distortion
and it does not introduce blocking artifacts.
Webly Supervised Learning for Skin Lesion Classification
Authors: Fernando Navarro, Sailesh Conjeti, Federico Tombari, Nassir Navab
Date: 2018-03-31 00:00:00
Tasks: Classification, General Classification, Lesion Classification, Skin Lesion Classification, Specificity, Transfer Learning

Within medical imaging, manual curation of sufficient well-labeled samples is cost, time and scale-prohibitive. To improve the representativeness of the training dataset, for the first time, we present an approach to utilize large amounts of freely available web data through web-crawling. To handle noise and weak nature of web annotations, we propose a two-step transfer learning based training process with a robust loss function, termed as Webly Supervised Learning (WSL) to train deep models for the task. We also leverage search by image to improve the search specificity of our web-crawling and reduce cross-domain noise. Within WSL, we explicitly model the noise structure between classes and incorporate it to selectively distill knowledge from the web data during model training. To demonstrate improved performance due to WSL, we benchmarked on a publicly available 10-class fine-grained skin lesion classification dataset and report a significant improvement of top-1 classification accuracy from 71.25 % to 80.53 % due to the incorporation of web-supervision.
A Subpixel Registration Algorithm for Low PSNR Images
Authors: Song Feng, Linhua Deng, Guofeng Shu, Feng Wang, Hui Deng, Kaifan Ji
Date: 2018-03-31 00:00:00
Tasks: Translation

This paper presents a fast algorithm for obtaining high-accuracy subpixel
translation of low PSNR images. Instead of locating the maximum point on the
upsampled images or fitting the peak of correlation surface, the proposed
algorithm is based on the measurement of centroid on the cross correlation
surface by Modified Moment method. Synthetic images, real solar images and
standard testing images with white Gaussian noise added were tested, and the
results show that the accuracies of our algorithm are comparable with other
subpixel registration techniques and the processing speed is higher. The
drawback is also discussed at the end of this paper.
Indian Buffet Process Deep Generative Models for Semi-Supervised Classification
Authors: Sotirios P. Chatzis
Date: 2014-02-14 00:00:00
Tasks: Classification, General Classification, Variational Inference

Deep generative models (DGMs) have brought about a major breakthrough, as
well as renewed interest, in generative latent variable models. However, DGMs
do not allow for performing data-driven inference of the number of latent
features needed to represent the observed data. Traditional linear formulations
address this issue by resorting to tools from the field of nonparametric
statistics. Indeed, linear latent variable models imposed an Indian Buffet
Process (IBP) prior have been extensively studied by the machine learning
community; inference for such models can been performed either via exact
sampling or via approximate variational techniques. Based on this inspiration,
in this paper we examine whether similar ideas from the field of Bayesian
nonparametrics can be utilized in the context of modern DGMs in order to
address the latent variable dimensionality inference problem. To this end, we
propose a novel DGM formulation, based on the imposition of an IBP prior. We
devise an efficient Black-Box Variational inference algorithm for our model,
and exhibit its efficacy in a number of semi-supervised classification
experiments. In all cases, we use popular benchmark datasets, and compare to
state-of-the-art DGMs.
Identifying Quantum Phase Transitions with Adversarial Neural Networks
Authors: Patrick Huembeli, Alexandre Dauphin, Peter Wittek
Date: 2017-10-11 00:00:00
Tasks: Domain Adaptation, Feature Engineering

The identification of phases of matter is a challenging task, especially in
quantum mechanics, where the complexity of the ground state appears to grow
exponentially with the size of the system. We address this problem with
state-of-the-art deep learning techniques: adversarial domain adaptation. We
derive the phase diagram of the whole parameter space starting from a fixed and
known subspace using unsupervised learning. The input data set contains both
labeled and unlabeled data instances. The first kind is a system that admits an
accurate analytical or numerical solution, and one can recover its phase
diagram. The second type is the physical system with an unknown phase diagram.
Adversarial domain adaptation uses both types of data to create invariant
feature extracting layers in a deep learning architecture. Once these layers
are trained, we can attach an unsupervised learner to the network to find phase
transitions. We show the success of this technique by applying it on several
paradigmatic models: the Ising model with different temperatures, the
Bose-Hubbard model, and the SSH model with disorder. The input is the ground
state without any manual feature engineering, and the dimension of the
parameter space is unrestricted. The method finds unknown transitions
successfully and predicts transition points in close agreement with standard
methods. This study opens the door to the classification of physical systems
where the phases boundaries are complex such as the many-body localization
problem or the Bose glass phase.
Hardware design of LIF with Latency neuron model with memristive STDP synapses
Authors: Simone Acciarito, Gian Carlo Cardarilli, Alessandro Cristini, Luca Di Nunzio, Rocco Fazzolari, Gaurav Mani Khanal, Marco Re, Gianluca Susi
Date: 2018-03-31 00:00:00

In this paper, the hardware implementation of a neuromorphic system is
presented. This system is composed of a Leaky Integrate-and-Fire with Latency
(LIFL) neuron and a Spike-Timing Dependent Plasticity (STDP) synapse. LIFL
neuron model allows to encode more information than the common
Integrate-and-Fire models, typically considered for neuromorphic
implementations. In our system LIFL neuron is implemented using CMOS circuits
while memristor is used for the implementation of the STDP synapse. A
description of the entire circuit is provided. Finally, the capabilities of the
proposed architecture have been evaluated by simulating a motif composed of
three neurons and two synapses. The simulation results confirm the validity of
the proposed system and its suitability for the design of more complex spiking
neural networks
Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue Modelling
Authors: Simon Keizer, Verena Rieser
Date: 2018-03-31 00:00:00
Tasks: Dialogue Management, Domain Adaptation, Management, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Spoken Dialogue Systems

Recent statistical approaches have improved the robustness and scalability of
spoken dialogue systems. However, despite recent progress in domain adaptation,
their reliance on in-domain data still limits their cross-domain scalability.
In this paper, we argue that this problem can be addressed by extending current
models to reflect and exploit the multi-dimensional nature of human dialogue.
We present our multi-dimensional, statistical dialogue management framework, in
which transferable conversational skills can be learnt by separating out
domain-independent dimensions of communication and using multi-agent
reinforcement learning. Our initial experiments with a simulated user show that
we can speed up the learning process by transferring learnt policies.
Generative Adversarial Networks (GANs): What it can generate and What it cannot?
Authors: P Manisha, Sujit Gujar
Date: 2018-03-31 00:00:00
Tasks: Articles

In recent years, Generative Adversarial Networks (GANs) have received significant attention from the research community. With a straightforward implementation and outstanding results, GANs have been used for numerous applications. Despite the success, GANs lack a proper theoretical explanation. These models suffer from issues like mode collapse, non-convergence, and instability during training. To address these issues, researchers have proposed theoretically rigorous frameworks inspired by varied fields of Game theory, Statistical theory, Dynamical systems, etc. In this paper, we propose to give an appropriate structure to study these contributions systematically. We essentially categorize the papers based on the issues they raise and the kind of novelty they introduce to address them. Besides, we provide insight into how each of the discussed articles solves the concerned problems. We compare and contrast different results and put forth a summary of theoretical contributions about GANs with focus on image/visual applications. We expect this summary paper to give a bird's eye view to a person wishing to understand the theoretical progress in GANs so far.
Word2Bits - Quantized Word Vectors
Authors: Maximilian Lam
Date: 2018-03-15 00:00:00
Tasks: Quantization, Question Answering, Word Similarity

Word vectors require significant amounts of memory and storage, posing issues
to resource limited devices like mobile phones and GPUs. We show that high
quality quantized word vectors using 1-2 bits per parameter can be learned by
introducing a quantization function into Word2Vec. We furthermore show that
training with the quantization function acts as a regularizer. We train word
vectors on English Wikipedia (2017) and evaluate them on standard word
similarity and analogy tasks and on question answering (SQuAD). Our quantized
word vectors not only take 8-16x less space than full precision (32 bit) word
vectors but also outperform them on word similarity tasks and question
answering.
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
Authors: Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González, Roberto J. López-Sastre
Date: 2017-09-07 00:00:00
Proceeding: AKBC 2019
Tasks: Graph Embedding, Image Retrieval, Knowledge Graph Embedding, Knowledge Graph Embeddings, Knowledge Graphs, Representation Learning, Retrieval, Zero-Shot Learning

A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images. We explore novel machine learning approaches for answering visual-relational queries in web-extracted knowledge graphs. To this end, we have created ImageGraph, a KG with 1,330 relation types, 14,870 entities, and 829,931 images crawled from the web. With visual-relational KGs such as ImageGraph one can introduce novel probabilistic query types in which images are treated as first-class citizens. Both the prediction of relations between unseen images as well as multi-relational image retrieval can be expressed with specific families of visual-relational queries. We introduce novel combinations of convolutional networks and knowledge graph embedding methods to answer such queries. We also explore a zero-shot learning scenario where an image of an entirely new entity is linked with multiple relations to entities of an existing KG. The resulting multi-relational grounding of unseen entity images into a knowledge graph serves as a semantic entity representation. We conduct experiments to demonstrate that the proposed methods can answer these visual-relational queries efficiently and accurately.
Locally Convex Sparse Learning over Networks
Authors: Ahmed Zaki, Saikat Chatterjee, Partha P. Mitra, Lars K. Rasmussen
Date: 2018-03-31 00:00:00
Tasks: Sparse Learning

We consider a distributed learning setup where a sparse signal is estimated
over a network. Our main interest is to save communication resource for
information exchange over the network and reduce processing time. Each node of
the network uses a convex optimization based algorithm that provides a locally
optimum solution for that node. The nodes exchange their signal estimates over
the network in order to refine their local estimates. At a node, the
optimization algorithm is based on an $\ell_1$-norm minimization with
appropriate modifications to promote sparsity as well as to include influence
of estimates from neighboring nodes. Our expectation is that local estimates in
each node improve fast and converge, resulting in a limited demand for
communication of estimates between nodes and reducing the processing time. We
provide restricted-isometry-property (RIP)-based theoretical analysis on
estimation quality. In the scenario of clean observation, it is shown that the
local estimates converge to the exact sparse signal under certain technical
conditions. Simulation results show that the proposed algorithms show
competitive performance compared to a globally optimum distributed LASSO
algorithm in the sense of convergence speed and estimation error.