pwc-archive/papers-with-abstracts huggingface dataset:
Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneously
Authors: Kyungjune Baek, Duhyeon Bang, Hyunjung Shim
Date: 2018-07-20 00:00:00
Tasks: Attribute, Face Generation

We propose a novel framework for simultaneously generating and manipulating
the face images with desired attributes. While the state-of-the-art attribute
editing technique has achieved the impressive performance for creating
realistic attribute effects, they only address the image editing problem, using
the input image as the condition of model. Recently, several studies attempt to
tackle both novel face generation and attribute editing problem using a single
solution. However, their image quality is still unsatisfactory. Our goal is to
develop a single unified model that can simultaneously create and edit high
quality face images with desired attributes. A key idea of our work is that we
decompose the image into the latent and attribute vector in low dimensional
representation, and then utilize the GAN framework for mapping the low
dimensional representation to the image. In this way, we can address both the
generation and editing problem by learning the generator. For qualitative and
quantitative evaluations, the proposed algorithm outperforms recent algorithms
addressing the same problem. Also, we show that our model can achieve the
competitive performance with the state-of-the-art attribute editing technique
in terms of attribute editing quality.
Automatic Semantic Content Removal by Learning to Neglect
Authors: Siyang Qin, Jiahui Wei, Roberto Manduchi
Date: 2018-07-20 00:00:00
Tasks: Decoder, Segmentation

We introduce a new system for automatic image content removal and inpainting.
Unlike traditional inpainting algorithms, which require advance knowledge of
the region to be filled in, our system automatically detects the area to be
removed and infilled. Region segmentation and inpainting are performed jointly
in a single pass. In this way, potential segmentation errors are more naturally
alleviated by the inpainting module. The system is implemented as an
encoder-decoder architecture, with two decoder branches, one tasked with
segmentation of the foreground region, the other with inpainting. The encoder
and the two decoder branches are linked via neglect nodes, which guide the
inpainting process in selecting which areas need reconstruction. The whole
model is trained using a conditional GAN strategy. Comparative experiments show
that our algorithm outperforms state-of-the-art inpainting techniques (which,
unlike our system, do not segment the input image and thus must be aided by an
external segmentation module.)
Toward Characteristic-Preserving Image-based Virtual Try-On Network
Authors: Bochao Wang, Huabin Zheng, Xiaodan Liang, Yimin Chen, Liang Lin, Meng Yang
Date: 2018-07-20 00:00:00
Proceeding: ECCV 2018 9
Tasks: Geometric Matching, Virtual Try-on

Image-based virtual try-on systems for fitting new in-shop clothes into a
person image have attracted increasing research attention, yet is still
challenging. A desirable pipeline should not only transform the target clothes
into the most fitting shape seamlessly but also preserve well the clothes
identity in the generated image, that is, the key characteristics (e.g.
texture, logo, embroidery) that depict the original clothes. However, previous
image-conditioned generation works fail to meet these critical requirements
towards the plausible virtual try-on performance since they fail to handle
large spatial misalignment between the input image and target clothes. Prior
work explicitly tackled spatial deformation using shape context matching, but
failed to preserve clothing details due to its coarse-to-fine strategy. In this
work, we propose a new fully-learnable Characteristic-Preserving Virtual Try-On
Network(CP-VTON) for addressing all real-world challenges in this task. First,
CP-VTON learns a thin-plate spline transformation for transforming the in-shop
clothes into fitting the body shape of the target person via a new Geometric
Matching Module (GMM) rather than computing correspondences of interest points
as prior works did. Second, to alleviate boundary artifacts of warped clothes
and make the results more realistic, we employ a Try-On Module that learns a
composition mask to integrate the warped clothes and the rendered image to
ensure smoothness. Extensive experiments on a fashion dataset demonstrate our
CP-VTON achieves the state-of-the-art virtual try-on performance both
qualitatively and quantitatively.
Bounding Box Embedding for Single Shot Person Instance Segmentation
Authors: Jacob Richeimer, Jonathan Mitchell
Date: 2018-07-20 00:00:00
Tasks: Instance Segmentation, Object, Segmentation, Semantic Segmentation

We present a bottom-up approach for the task of object instance segmentation
using a single-shot model. The proposed model employs a fully convolutional
network which is trained to predict class-wise segmentation masks as well as
the bounding boxes of the object instances to which each pixel belongs. This
allows us to group object pixels into individual instances. Our network
architecture is based on the DeepLabv3+ model, and requires only minimal extra
computation to achieve pixel-wise instance assignments. We apply our method to
the task of person instance segmentation, a common task relevant to many
applications. We train our model with COCO data and report competitive results
for the person class in the COCO instance segmentation task.
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
Authors: Sungryull Sohn, Junhyuk Oh, Honglak Lee
Date: 2018-07-19 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Hierarchical Reinforcement Learning, Network Embedding, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Zero-shot Generalization

We introduce a new RL problem where the agent is required to generalize to a previously-unseen environment characterized by a subtask graph which describes a set of subtasks and their dependencies. Unlike existing hierarchical multitask RL approaches that explicitly describe what the agent should do at a high level, our problem only describes properties of subtasks and relationships among them, which requires the agent to perform complex reasoning to find the optimal subtask to execute. To solve this problem, we propose a neural subtask graph solver (NSGS) which encodes the subtask graph using a recursive neural network embedding. To overcome the difficulty of training, we propose a novel non-parametric gradient-based policy, graph reward propagation, to pre-train our NSGS agent and further finetune it through actor-critic method. The experimental results on two 2D visual domains show that our agent can perform complex reasoning to find a near-optimal way of executing the subtask graph and generalize well to the unseen subtask graphs. In addition, we compare our agent with a Monte-Carlo tree search (MCTS) method showing that our method is much more efficient than MCTS, and the performance of NSGS can be further improved by combining it with MCTS.
Automatically Designing CNN Architectures for Medical Image Segmentation
Authors: Aliasghar Mortazi, Ulas Bagci
Date: 2018-07-19 00:00:00
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Reinforcement Learning, Segmentation, Semantic Segmentation

Deep neural network architectures have traditionally been designed and
explored with human expertise in a long-lasting trial-and-error process. This
process requires huge amount of time, expertise, and resources. To address this
tedious problem, we propose a novel algorithm to optimally find hyperparameters
of a deep network architecture automatically. We specifically focus on
designing neural architectures for medical image segmentation task. Our
proposed method is based on a policy gradient reinforcement learning for which
the reward function is assigned a segmentation evaluation utility (i.e., dice
index). We show the efficacy of the proposed method with its low computational
cost in comparison with the state-of-the-art medical image segmentation
networks. We also present a new architecture design, a densely connected
encoder-decoder CNN, as a strong baseline architecture to apply the proposed
hyperparameter search algorithm. We apply the proposed algorithm to each layer
of the baseline architectures. As an application, we train the proposed system
on cine cardiac MR images from Automated Cardiac Diagnosis Challenge (ACDC)
MICCAI 2017. Starting from a baseline segmentation architecture, the resulting
network architecture obtains the state-of-the-art results in accuracy without
performing any trial-and-error based architecture design approaches or close
supervision of the hyperparameters changes.
Deriving star cluster parameters with convolutional neural networks. I. Age, mass, and size
Authors: J. Bialopetravičius, D. Narbutis, V. Vansevičius
Date: 2018-07-19 00:00:00

Context. Convolutional neural networks (CNNs) have been proven to perform
fast classification and detection on natural images and have potential to infer
astrophysical parameters on the exponentially increasing amount of sky survey
imaging data. The inference pipeline can be trained either from real
human-annotated data or simulated mock observations. Until now star cluster
analysis was based on integral or individual resolved stellar photometry. This
limits the amount of information that can be extracted from cluster images.
  Aims. Develop a CNN-based algorithm aimed to simultaneously derive ages,
masses, and sizes of star clusters directly from multi-band images. Demonstrate
CNN capabilities on low mass semi-resolved star clusters in a low
signal-to-noise ratio regime.
  Methods. A CNN was constructed based on the deep residual network (ResNet)
architecture and trained on simulated images of star clusters with various
ages, masses, and sizes. To provide realistic backgrounds, M31 star fields
taken from the PHAT survey were added to the mock cluster images.
  Results. The proposed CNN was verified on mock images of artificial clusters
and has demonstrated high precision and no significant bias for clusters of
ages $\lesssim$3Gyr and masses between 250 and 4,000 ${\rm M_\odot}$. The
pipeline is end-to-end, starting from input images all the way to the inferred
parameters; no hand-coded steps have to be performed: estimates of parameters
are provided by the neural network in one inferential step from raw images.
Rapid Time Series Prediction with a Hardware-Based Reservoir Computer
Authors: Daniel Canaday, Aaron Griffith, Daniel Gauthier
Date: 2018-07-19 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

Reservoir computing is a neural network approach for processing
time-dependent signals that has seen rapid development in recent years.
Physical implementations of the technique using optical reservoirs have
demonstrated remarkable accuracy and processing speed at benchmark tasks.
However, these approaches require an electronic output layer to maintain high
performance, which limits their use in tasks such as time-series prediction,
where the output is fed back into the reservoir. We present here a reservoir
computing scheme that has rapid processing speed both by the reservoir and the
output layer. The reservoir is realized by an autonomous, time-delay, Boolean
network configured on a field-programmable gate array. We investigate the
dynamical properties of the network and observe the fading memory property that
is critical for successful reservoir computing. We demonstrate the utility of
the technique by training a reservoir to learn the short- and long-term
behavior of a chaotic system. We find accuracy comparable to state-of-the-art
software approaches of similar network size, but with a superior real-time
prediction rate up to 160 MHz.
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits
Authors: Julian Zimmert, Yevgeny Seldin
Date: 2018-07-19 00:00:00
Tasks: Multi-Armed Bandits, Thompson Sampling

We derive an algorithm that achieves the optimal (within constants) pseudo-regret in both adversarial and stochastic multi-armed bandits without prior knowledge of the regime and time horizon. The algorithm is based on online mirror descent (OMD) with Tsallis entropy regularization with power $\alpha=1/2$ and reduced-variance loss estimators. More generally, we define an adversarial regime with a self-bounding constraint, which includes stochastic regime, stochastically constrained adversarial regime (Wei and Luo), and stochastic regime with adversarial corruptions (Lykouris et al.) as special cases, and show that the algorithm achieves logarithmic regret guarantee in this regime and all of its special cases simultaneously with the adversarial regret guarantee.} The algorithm also achieves adversarial and stochastic optimality in the utility-based dueling bandit setting. We provide empirical evaluation of the algorithm demonstrating that it significantly outperforms UCB1 and EXP3 in stochastic environments. We also provide examples of adversarial environments, where UCB1 and Thompson Sampling exhibit almost linear regret, whereas our algorithm suffers only logarithmic regret. To the best of our knowledge, this is the first example demonstrating vulnerability of Thompson Sampling in adversarial environments. Last, but not least, we present a general stochastic analysis and a general adversarial analysis of OMD algorithms with Tsallis entropy regularization for $\alpha\in[0,1]$ and explain the reason why $\alpha=1/2$ works best.
Approximate Collapsed Gibbs Clustering with Expectation Propagation
Authors: Christopher Aicher, Emily B. Fox
Date: 2018-07-19 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis, Time Series Clustering

We develop a framework for approximating collapsed Gibbs sampling in
generative latent variable cluster models. Collapsed Gibbs is a popular MCMC
method, which integrates out variables in the posterior to improve mixing.
Unfortunately for many complex models, integrating out these variables is
either analytically or computationally intractable. We efficiently approximate
the necessary collapsed Gibbs integrals by borrowing ideas from expectation
propagation. We present two case studies where exact collapsed Gibbs sampling
is intractable: mixtures of Student-t's and time series clustering. Our
experiments on real and synthetic data show that our approximate sampler
enables a runtime-accuracy tradeoff in sampling these types of models,
providing results with competitive accuracy much more rapidly than the naive
Gibbs samplers one would otherwise rely on in these scenarios.
Adaptive Variational Particle Filtering in Non-stationary Environments
Authors: Mahdi Azarafrooz
Date: 2018-07-19 00:00:00

Online convex optimization is a sequential prediction framework with the goal
to track and adapt to the environment through evaluating proper convex loss
functions. We study efficient particle filtering methods from the perspective
of such a framework.
  We formulate an efficient particle filtering methods for the non-stationary
environment by making connections with the online mirror descent algorithm
which is known to be a universal online convex optimization algorithm.
  As a result of this connection, our proposed particle filtering algorithm
proves to achieve optimal particle efficiency.
Unsupervised Metric Learning in Presence of Missing Data
Authors: Anna C. Gilbert, Rishi Sonthalia
Date: 2018-07-19 00:00:00
Tasks: Dimensionality Reduction, Matrix Completion, Metric Learning

For many machine learning tasks, the input data lie on a low-dimensional
manifold embedded in a high dimensional space and, because of this
high-dimensional structure, most algorithms are inefficient. The typical
solution is to reduce the dimension of the input data using standard dimension
reduction algorithms such as ISOMAP, LAPLACIAN EIGENMAPS or LLES. This
approach, however, does not always work in practice as these algorithms require
that we have somewhat ideal data. Unfortunately, most data sets either have
missing entries or unacceptably noisy values. That is, real data are far from
ideal and we cannot use these algorithms directly. In this paper, we focus on
the case when we have missing data. Some techniques, such as matrix completion,
can be used to fill in missing data but these methods do not capture the
non-linear structure of the manifold. Here, we present a new algorithm
MR-MISSING that extends these previous algorithms and can be used to compute
low dimensional representation on data sets with missing entries. We
demonstrate the effectiveness of our algorithm by running three different
experiments. We visually verify the effectiveness of our algorithm on synthetic
manifolds, we numerically compare our projections against those computed by
first filling in data using nlPCA and mDRUR on the MNIST data set, and we also
show that we can do classification on MNIST with missing data. We also provide
a theoretical guarantee for MR-MISSING under some simplifying assumptions.
Doubly Stochastic Adversarial Autoencoder
Authors: Mahdi Azarafrooz
Date: 2018-07-19 00:00:00
Proceeding: ICLR 2018 1
Tasks: continuous-control, Continuous Control

Any autoencoder network can be turned into a generative model by imposing an
arbitrary prior distribution on its hidden code vector. Variational Autoencoder
(VAE) [2] uses a KL divergence penalty to impose the prior, whereas Adversarial
Autoencoder (AAE) [1] uses {\it generative adversarial networks} GAN [3]. GAN
trades the complexities of {\it sampling} algorithms with the complexities of
{\it searching} Nash equilibrium in minimax games. Such minimax architectures
get trained with the help of data examples and gradients flowing through a
generator and an adversary. A straightforward modification of AAE is to replace
the adversary with the maximum mean discrepancy (MMD) test [4-5]. This
replacement leads to a new type of probabilistic autoencoder, which is also
discussed in our paper. We propose a novel probabilistic autoencoder in which
the adversary of AAE is replaced with a space of {\it stochastic} functions.
This replacement introduces a new source of randomness, which can be considered
as a continuous control for encouraging {\it explorations}. This prevents the
adversary from fitting too closely to the generator and therefore leads to a
more diverse set of generated samples.
The Deep Kernelized Autoencoder
Authors: Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi, Robert Jenssen, Lorenzo Livi
Date: 2018-07-19 00:00:00
Tasks: Denoising

Autoencoders learn data representations (codes) in such a way that the input
is reproduced at the output of the network. However, it is not always clear
what kind of properties of the input data need to be captured by the codes.
Kernel machines have experienced great success by operating via inner-products
in a theoretically well-defined reproducing kernel Hilbert space, hence
capturing topological properties of input data. In this paper, we enhance the
autoencoder's ability to learn effective data representations by aligning inner
products between codes with respect to a kernel matrix. By doing so, the
proposed kernelized autoencoder allows learning similarity-preserving
embeddings of input data, where the notion of similarity is explicitly
controlled by the user and encoded in a positive semi-definite kernel matrix.
Experiments are performed for evaluating both reconstruction and kernel
alignment performance in classification tasks and visualization of
high-dimensional data. Additionally, we show that our method is capable to
emulate kernel principal component analysis on a denoising task, obtaining
competitive results at a much lower computational cost.
Fully Convolutional Pixel Adaptive Image Denoiser
Authors: Sungmin Cha, Taesup Moon
Date: 2018-07-19 00:00:00
Proceeding: ICCV 2019 10
Tasks: Denoising, Image Denoising

We propose a new image denoising algorithm, dubbed as Fully Convolutional Adaptive Image DEnoiser (FC-AIDE), that can learn from an offline supervised training set with a fully convolutional neural network as well as adaptively fine-tune the supervised model for each given noisy image. We significantly extend the framework of the recently proposed Neural AIDE, which formulates the denoiser to be context-based pixelwise mappings and utilizes the unbiased estimator of MSE for such denoisers. The two main contributions we make are; 1) implementing a novel fully convolutional architecture that boosts the base supervised model, and 2) introducing regularization methods for the adaptive fine-tuning such that a stronger and more robust adaptivity can be attained. As a result, FC-AIDE is shown to possess many desirable features; it outperforms the recent CNN-based state-of-the-art denoisers on all of the benchmark datasets we tested, and gets particularly strong for various challenging scenarios, e.g., with mismatched image/noise characteristics or with scarce supervised training data. The source code of our algorithm is available at https://github.com/csm9493/FC-AIDE-Keras.
Towards a general mathematical theory of experimental science
Authors: Gabriele Carcassi, Christine A. Aidala
Date: 2018-06-22 00:00:00

We lay the groundwork for a formal framework that studies scientific theories
and can serve as a unified foundation for the different theories within
physics. We define a scientific theory as a set of verifiable statements,
assertions that can be shown to be true with an experimental test in finite
time. By studying the algebra of such objects, we show that verifiability
already provides severe constraints. In particular, it requires that a set of
physically distinguishable cases is naturally equipped with the mathematical
structures (i.e. second-countable Kolmogorov topologies and $\sigma$-algebras)
that form the foundation of manifold theory, differential geometry, measure
theory, probability theory and all the major branches of mathematics currently
used in physics. This gives a clear physical meaning to those mathematical
structures and provides a strong justification for their use in science. Most
importantly it provides a formal framework to incorporate additional
assumptions and constrain the search space for new physical theories.
Sharper asset ranking from total drawdown durations
Authors: Damien Challet
Date: 2015-05-06 00:00:00

The total duration of drawdowns is shown to provide a moment-free, unbiased,
efficient and robust estimator of Sharpe ratios both for Gaussian and
heavy-tailed price returns. We then use this quantity to infer an analytic
expression of the bias of moment-based Sharpe ratio estimators as a function of
the return distribution tail exponent. The heterogeneity of tail exponents at
any given time among assets implies that our new method yields significantly
different asset rankings than those of moment-based methods, especially in
periods large volatility. This is fully confirmed by using 20 years of
historical data on 3449 liquid US equities.
Named Entity Recognition with Extremely Limited Data
Authors: John Foley, Sheikh Muhammad Sarwar, James Allan
Date: 2018-06-12 00:00:00
Tasks: Information Retrieval, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Retrieval

Traditional information retrieval treats named entity recognition as a
pre-indexing corpus annotation task, allowing entity tags to be indexed and
used during search. Named entity taggers themselves are typically trained on
thousands or tens of thousands of examples labeled by humans.
  However, there is a long tail of named entities classes, and for these cases,
labeled data may be impossible to find or justify financially. We propose
exploring named entity recognition as a search task, where the named entity
class of interest is a query, and entities of that class are the relevant
"documents". What should that query look like? Can we even perform NER-style
labeling with tens of labels? This study presents an exploration of CRF-based
NER models with handcrafted features and of how we might transform them into
search queries.