pwc-archive/papers-with-abstracts huggingface dataset:
Fashion++: Minimal Edits for Outfit Improvement
Authors: Wei-Lin Hsiao, Isay Katsman, Chao-yuan Wu, Devi Parikh, Kristen Grauman
Date: 2019-04-19 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Generation

Given an outfit, what small changes would most improve its fashionability? This question presents an intriguing new vision challenge. We introduce Fashion++, an approach that proposes minimal adjustments to a full-body clothing outfit that will have maximal impact on its fashionability. Our model consists of a deep image generation neural network that learns to synthesize clothing conditioned on learned per-garment encodings. The latent encodings are explicitly factorized according to shape and texture, thereby allowing direct edits for both fit/presentation and color/patterns/material, respectively. We show how to bootstrap Web photos to automatically train a fashionability model, and develop an activation maximization-style approach to transform the input image into its more fashionable self. The edits suggested range from swapping in a new garment to tweaking its color, how it is worn (e.g., rolling up sleeves), or its fit (e.g., making pants baggier). Experiments demonstrate that Fashion++ provides successful edits, both according to automated metrics and human opinion. Project page is at http://vision.cs.utexas.edu/projects/FashionPlus.
On the Convergence of Adam and Beyond
Authors: Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
Date: 2019-04-19 00:00:00
Proceeding: ICLR 2018 1
Tasks: Stochastic Optimization

Several recently proposed stochastic optimization methods that have been
successfully used in training deep networks such as RMSProp, Adam, Adadelta,
Nadam are based on using gradient updates scaled by square roots of exponential
moving averages of squared past gradients. In many applications, e.g. learning
with large output spaces, it has been empirically observed that these
algorithms fail to converge to an optimal solution (or a critical point in
nonconvex settings). We show that one cause for such failures is the
exponential moving average used in the algorithms. We provide an explicit
example of a simple convex optimization setting where Adam does not converge to
the optimal solution, and describe the precise problems with the previous
analysis of Adam algorithm. Our analysis suggests that the convergence issues
can be fixed by endowing such algorithms with `long-term memory' of past
gradients, and propose new variants of the Adam algorithm which not only fix
the convergence issues but often also lead to improved empirical performance.
Runtime Analysis of the Univariate Marginal Distribution Algorithm under Low Selective Pressure and Prior Noise
Authors: Per Kristian Lehre, Phan Trung Hai Nguyen
Date: 2019-04-19 00:00:00

We perform a rigorous runtime analysis for the Univariate Marginal
Distribution Algorithm on the LeadingOnes function, a well-known benchmark
function in the theory community of evolutionary computation with a high
correlation between decision variables. For a problem instance of size $n$, the
currently best known upper bound on the expected runtime is
$\mathcal{O}(n\lambda\log\lambda+n^2)$ (Dang and Lehre, GECCO 2015), while a
lower bound necessary to understand how the algorithm copes with variable
dependencies is still missing. Motivated by this, we show that the algorithm
requires a $e^{\Omega(\mu)}$ runtime with high probability and in expectation
if the selective pressure is low; otherwise, we obtain a lower bound of
$\Omega(\frac{n\lambda}{\log(\lambda-\mu)})$ on the expected runtime.
Furthermore, we for the first time consider the algorithm on the function under
a prior noise model and obtain an $\mathcal{O}(n^2)$ expected runtime for the
optimal parameter settings. In the end, our theoretical results are accompanied
by empirical findings, not only matching with rigorous analyses but also
providing new insights into the behaviour of the algorithm.
Reliable Multi-label Classification: Prediction with Partial Abstention
Authors: Vu-Linh Nguyen, Eyke Hüllermeier
Date: 2019-04-19 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Prediction

In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a subset of all labels. In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. We propose a formalization of MLC with abstention in terms of a generalized loss minimization problem and present first results for the case of the Hamming loss, rank loss, and F-measure, both theoretical and experimental.
XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation
Authors: Youbao Tang, Yu-Xing Tang, Jing Xiao, Ronald M. Summers
Date: 2019-04-19 00:00:00
Tasks: Data Augmentation, Image Generation, Image-to-Image Translation, Segmentation, Translation

This paper proposes a novel framework for lung segmentation in chest X-rays.
It consists of two key contributions, a criss-cross attention based
segmentation network and radiorealistic chest X-ray image synthesis (i.e. a
synthesized radiograph that appears anatomically realistic) for data
augmentation. The criss-cross attention modules capture rich global contextual
information in both horizontal and vertical directions for all the pixels thus
facilitating accurate lung segmentation. To reduce the manual annotation burden
and to train a robust lung segmentor that can be adapted to pathological lungs
with hazy lung boundaries, an image-to-image translation module is employed to
synthesize radiorealistic abnormal CXRs from the source of normal ones for data
augmentation. The lung masks of synthetic abnormal CXRs are propagated from the
segmentation results of their normal counterparts, and then serve as pseudo
masks for robust segmentor training. In addition, we annotate 100 CXRs with
lung masks on a more challenging NIH Chest X-ray dataset containing both
posterioranterior and anteroposterior views for evaluation. Extensive
experiments validate the robustness and effectiveness of the proposed
framework. The code and data can be found from
https://github.com/rsummers11/CADLab/tree/master/Lung_Segmentation_XLSor .
Uncertainty about Uncertainty: Optimal Adaptive Algorithms for Estimating Mixtures of Unknown Coins
Authors: Jasper C. H. Lee, Paul Valiant
Date: 2019-04-19 00:00:00
Tasks: LEMMA

Given a mixture between two populations of coins, "positive" coins that each have -- unknown and potentially different -- bias $\geq\frac{1}{2}+\Delta$ and "negative" coins with bias $\leq\frac{1}{2}-\Delta$, we consider the task of estimating the fraction $\rho$ of positive coins to within additive error $\epsilon$. We achieve an upper and lower bound of $\Theta(\frac{\rho}{\epsilon^2\Delta^2}\log\frac{1}{\delta})$ samples for a $1-\delta$ probability of success, where crucially, our lower bound applies to all fully-adaptive algorithms. Thus, our sample complexity bounds have tight dependence for every relevant problem parameter. A crucial component of our lower bound proof is a decomposition lemma (see Lemmas 17 and 18) showing how to assemble partially-adaptive bounds into a fully-adaptive bound, which may be of independent interest: though we invoke it for the special case of Bernoulli random variables (coins), it applies to general distributions. We present simulation results to demonstrate the practical efficacy of our approach for realistic problem parameters for crowdsourcing applications, focusing on the "rare events" regime where $\rho$ is small. The fine-grained adaptive flavor of both our algorithm and lower bound contrasts with much previous work in distributional testing and learning.
ERNIE: Enhanced Representation through Knowledge Integration
Authors: Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu
Date: 2019-04-19 00:00:00
Tasks: Chinese Named Entity Recognition, Chinese Sentence Pair Classification, Chinese Sentiment Analysis, Cloze Test, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Inference, Question Answering, Semantic Similarity, Semantic Textual Similarity, Sentiment Analysis

We present a novel language representation model enhanced by knowledge called
ERNIE (Enhanced Representation through kNowledge IntEgration). Inspired by the
masking strategy of BERT, ERNIE is designed to learn language representation
enhanced by knowledge masking strategies, which includes entity-level masking
and phrase-level masking. Entity-level strategy masks entities which are
usually composed of multiple words.Phrase-level strategy masks the whole phrase
which is composed of several words standing together as a conceptual
unit.Experimental results show that ERNIE outperforms other baseline methods,
achieving new state-of-the-art results on five Chinese natural language
processing tasks including natural language inference, semantic similarity,
named entity recognition, sentiment analysis and question answering. We also
demonstrate that ERNIE has more powerful knowledge inference capacity on a
cloze test.
Risk Convergence of Centered Kernel Ridge Regression with Large Dimensional Data
Authors: Khalil Elkhalil, Abla Kammoun, Xiangliang Zhang, Mohamed-Slim Alouini, Tareq Al-Naffouri
Date: 2019-04-19 00:00:00
Tasks: regression

This paper carries out a large dimensional analysis of a variation of kernel
ridge regression that we call \emph{centered kernel ridge regression} (CKRR),
also known in the literature as kernel ridge regression with offset. This
modified technique is obtained by accounting for the bias in the regression
problem resulting in the old kernel ridge regression but with \emph{centered}
kernels. The analysis is carried out under the assumption that the data is
drawn from a Gaussian distribution and heavily relies on tools from random
matrix theory (RMT). Under the regime in which the data dimension and the
training size grow infinitely large with fixed ratio and under some mild
assumptions controlling the data statistics, we show that both the empirical
and the prediction risks converge to a deterministic quantities that describe
in closed form fashion the performance of CKRR in terms of the data statistics
and dimensions. Inspired by this theoretical result, we subsequently build a
consistent estimator of the prediction risk based on the training data which
allows to optimally tune the design parameters. A key insight of the proposed
analysis is the fact that asymptotically a large class of kernels achieve the
same minimum prediction risk. This insight is validated with both synthetic and
real data.
Analyzing the benefits of communication channels between deep learning models
Authors: Philippe Lacaille
Date: 2019-04-19 00:00:00
Tasks: Deep Learning

As artificial intelligence systems spread to more diverse and larger tasks in
many domains, the machine learning algorithms, and in particular the deep
learning models and the databases required to train them are getting bigger
themselves. Some algorithms do allow for some scaling of large computations by
leveraging data parallelism. However, they often require a large amount of data
to be exchanged in order to ensure the shared knowledge throughout the compute
nodes is accurate.
  In this work, the effect of different levels of communications between deep
learning models is studied, in particular how it affects performance. The first
approach studied looks at decentralizing the numerous computations that are
done in parallel in training procedures such as synchronous and asynchronous
stochastic gradient descent. In this setting, a simplified communication that
consists of exchanging low bandwidth outputs between compute nodes can be
beneficial. In the following chapter, the communication protocol is slightly
modified to further include training instructions. Indeed, this is studied in a
simplified setup where a pre-trained model, analogous to a teacher, can
customize a randomly initialized model's training procedure to accelerate
learning. Finally, a communication channel where two deep learning models can
exchange a purposefully crafted language is explored while allowing for
different ways of optimizing that language.
Visualizing the decision-making process in deep neural decision forest
Authors: Shichao Li, Kwang-Ting Cheng
Date: 2019-04-19 00:00:00
Tasks: Decision Making, General Classification, regression, Representation Learning

Deep neural decision forest (NDF) achieved remarkable performance on various
vision tasks via combining decision tree and deep representation learning. In
this work, we first trace the decision-making process of this model and
visualize saliency maps to understand which portion of the input influence it
more for both classification and regression problems. We then apply NDF on a
multi-task coordinate regression problem and demonstrate the distribution of
routing probabilities, which is vital for interpreting NDF yet not shown for
regression problems. The pre-trained model and code for visualization will be
available at https://github.com/Nicholasli1995/VisualizingNDF
Video Object Segmentation and Tracking: A Survey
Authors: Rui Yao, Guosheng Lin, Shixiong Xia, Jiaqi Zhao, Yong Zhou
Date: 2019-04-19 00:00:00
Tasks: Autonomous Vehicles, Object, Object Tracking, Segmentation, Semantic Segmentation, Survey, Video Compression, Video Object Segmentation, Video Semantic Segmentation, Video Summarization

Object segmentation and object tracking are fundamental research area in the
computer vision community. These two topics are diffcult to handle some common
challenges, such as occlusion, deformation, motion blur, and scale variation.
The former contains heterogeneous object, interacting object, edge ambiguity,
and shape complexity. And the latter suffers from difficulties in handling fast
motion, out-of-view, and real-time processing. Combining the two problems of
video object segmentation and tracking (VOST) can overcome their respective
difficulties and improve their performance. VOST can be widely applied to many
practical applications such as video summarization, high definition video
compression, human computer interaction, and autonomous vehicles. This article
aims to provide a comprehensive review of the state-of-the-art tracking
methods, and classify these methods into different categories, and identify new
trends. First, we provide a hierarchical categorization existing approaches,
including unsupervised VOS, semi-supervised VOS, interactive VOS, weakly
supervised VOS, and segmentation-based tracking methods. Second, we provide a
detailed discussion and overview of the technical characteristics of the
different methods. Third, we summarize the characteristics of the related video
dataset, and provide a variety of evaluation metrics. Finally, we point out a
set of interesting future works and draw our own conclusions.
Critically Examining the "Neural Hype": Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models
Authors: Wei Yang, Kuang Lu, Peilin Yang, Jimmy Lin
Date: 2019-04-19 00:00:00
Tasks: Retrieval

Is neural IR mostly hype? In a recent SIGIR Forum article, Lin expressed skepticism that neural ranking models were actually improving ad hoc retrieval effectiveness in limited data scenarios. He provided anecdotal evidence that authors of neural IR papers demonstrate "wins" by comparing against weak baselines. This paper provides a rigorous evaluation of those claims in two ways: First, we conducted a meta-analysis of papers that have reported experimental results on the TREC Robust04 test collection. We do not find evidence of an upward trend in effectiveness over time. In fact, the best reported results are from a decade ago and no recent neural approach comes close. Second, we applied five recent neural models to rerank the strong baselines that Lin used to make his arguments. A significant improvement was observed for one of the models, demonstrating additivity in gains. While there appears to be merit to neural IR approaches, at least some of the gains reported in the literature appear illusory.
Realistic Hair Simulation Using Image Blending
Authors: Mohamed Attia, Mohammed Hossny, Saeid Nahavandi, Anousha Yazdabadi, Hamed Asadi
Date: 2019-04-19 00:00:00
Tasks: Benchmarking, Data Augmentation, Diagnostic

In this presented work, we propose a realistic hair simulator using image
blending for dermoscopic images. This hair simulator can be used for
benchmarking and validation of the hair removal methods and in data
augmentation for improving computer aided diagnostic tools. We adopted one of
the popular implementation of image blending to superimpose realistic hair
masks to hair lesion. This method was able to produce realistic hair masks
according to a predefined mask for hair. Thus, the produced hair images and
masks can be used as ground truth for hair segmentation and removal methods by
inpainting hair according to a pre-defined hair masks on hairfree areas. Also,
we achieved a realism score equals to 1.65 in comparison to 1.59 for the
state-of-the-art hair simulator.
Transfer Entropy: where Shannon meets Turing
Authors: David Sigtermans
Date: 2019-04-19 00:00:00
Tasks: Time Series, Time Series Analysis

Transfer entropy is capable of capturing nonlinear source-destination relations between multi-variate time series. It is a measure of association between source data that are transformed into destination data via a set of linear transformations between their probability mass functions. The resulting tensor formalism is used to show that in specific cases, e.g., in the case the system consists of three stochastic processes, bivariate analysis suffices to distinguish true relations from false relations. This allows us to determine the causal structure as far as encoded in the probability mass functions of noisy data. The tensor formalism was also used to derive the Data Processing Inequality for transfer entropy.
Assessing the Sharpness of Satellite Images: Study of the PlanetScope Constellation
Authors: Jérémy Anger, Carlo de Franchis, Gabriele Facciolo
Date: 2019-04-19 00:00:00

New micro-satellite constellations enable unprecedented systematic monitoring
applications thanks to their wide coverage and short revisit capabilities.
However, the large volumes of images that they produce have uneven qualities,
creating the need for automatic quality assessment methods. In this work, we
quantify the sharpness of images from the PlanetScope constellation by
estimating the blur kernel from each image. Once the kernel has been estimated,
it is possible to compute an absolute measure of sharpness which allows to
discard low quality images and deconvolve blurry images before any further
processing. The method is fully blind and automatic, and since it does not
require the knowledge of any satellite specifications it can be ported to other
constellations.
Efficient Blind Deblurring under High Noise Levels
Authors: Jérémy Anger, Mauricio Delbracio, Gabriele Facciolo
Date: 2019-04-19 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Denoising, Image Deblurring, Vocal Bursts Intensity Prediction

The goal of blind image deblurring is to recover a sharp image from a motion blurred one without knowing the camera motion. Current state-of-the-art methods have a remarkably good performance on images with no noise or very low noise levels. However, the noiseless assumption is not realistic considering that low light conditions are the main reason for the presence of motion blur due to requiring longer exposure times. In fact, motion blur and high to moderate noise often appear together. Most works approach this problem by first estimating the blur kernel $k$ and then deconvolving the noisy blurred image. In this work, we first show that current state-of-the-art kernel estimation methods based on the $\ell_0$ gradient prior can be adapted to handle high noise levels while keeping their efficiency. Then, we show that a fast non-blind deconvolution method can be significantly improved by first denoising the blurry image. The proposed approach yields results that are equivalent to those obtained with much more computationally demanding methods.
A Scalable Handwritten Text Recognition System
Authors: R. Reeve Ingle, Yasuhisa Fujii, Thomas Deselaers, Jonathan Baccash, Ashok C. Popat
Date: 2019-04-19 00:00:00
Tasks: Handwriting Recognition, Handwritten Text Recognition, HTR, Optical Character Recognition (OCR)

Many studies on (Offline) Handwritten Text Recognition (HTR) systems have focused on building state-of-the-art models for line recognition on small corpora. However, adding HTR capability to a large scale multilingual OCR system poses new challenges. This paper addresses three problems in building such systems: data, efficiency, and integration. Firstly, one of the biggest challenges is obtaining sufficient amounts of high quality training data. We address the problem by using online handwriting data collected for a large scale production online handwriting recognition system. We describe our image data generation pipeline and study how online data can be used to build HTR models. We show that the data improve the models significantly under the condition where only a small number of real images is available, which is usually the case for HTR models. It enables us to support a new script at substantially lower cost. Secondly, we propose a line recognition model based on neural networks without recurrent connections. The model achieves a comparable accuracy with LSTM-based models while allowing for better parallelism in training and inference. Finally, we present a simple way to integrate HTR models into an OCR system. These constitute a solution to bring HTR capability into a large scale OCR system.
Knowledge Distillation via Route Constrained Optimization
Authors: Xiao Jin, Baoyun Peng, Yi-Chao Wu, Yu Liu, Jiaheng Liu, Ding Liang, Xiaolin Hu
Date: 2019-04-19 00:00:00
Proceeding: ICCV 2019 10
Tasks: Face Recognition, Knowledge Distillation

Distillation-based learning boosts the performance of the miniaturized neural
network based on the hypothesis that the representation of a teacher model can
be used as structured and relatively weak supervision, and thus would be easily
learned by a miniaturized model. However, we find that the representation of a
converged heavy model is still a strong constraint for training a small student
model, which leads to a high lower bound of congruence loss. In this work,
inspired by curriculum learning we consider the knowledge distillation from the
perspective of curriculum learning by routing. Instead of supervising the
student model with a converged teacher model, we supervised it with some anchor
points selected from the route in parameter space that the teacher model passed
by, as we called route constrained optimization (RCO). We experimentally
demonstrate this simple operation greatly reduces the lower bound of congruence
loss for knowledge distillation, hint and mimicking learning. On close-set
classification tasks like CIFAR100 and ImageNet, RCO improves knowledge
distillation by 2.14% and 1.5% respectively. For the sake of evaluating the
generalization, we also test RCO on the open-set face recognition task
MegaFace.