pwc-archive/papers-with-abstracts huggingface dataset:
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
Authors: Tim Pearce, Felix Leibfried, Alexandra Brintrup, Mohamed Zaki, Andy Neely
Date: 2018-10-12 00:00:00
Tasks: Bayesian Inference, General Classification, Image Classification, Uncertainty Quantification

Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes. The Bayesian framework provides a principled approach to this, however applying it to NNs is challenging due to large numbers of parameters and data. Ensembling NNs provides an easily implementable, scalable method for uncertainty quantification, however, it has been criticised for not being Bayesian. This work proposes one modification to the usual process that we argue does result in approximate Bayesian inference; regularising parameters about values drawn from a distribution which can be set equal to the prior. A theoretical analysis of the procedure in a simplified setting suggests the recovered posterior is centred correctly but tends to have an underestimated marginal variance, and overestimated correlation. However, two conditions can lead to exact recovery. We argue that these conditions are partially present in NNs. Empirical evaluations demonstrate it has an advantage over standard ensembling, and is competitive with variational methods.
Predictive Uncertainty through Quantization
Authors: Bastiaan S. Veeling, Rianne van den Berg, Max Welling
Date: 2018-10-12 00:00:00
Tasks: Quantization

High-risk domains require reliable confidence estimates from predictive
models. Deep latent variable models provide these, but suffer from the rigid
variational distributions used for tractable inference, which err on the side
of overconfidence. We propose Stochastic Quantized Activation Distributions
(SQUAD), which imposes a flexible yet tractable distribution over discretized
latent variables. The proposed method is scalable, self-normalizing and sample
efficient. We demonstrate that the model fully utilizes the flexible
distribution, learns interesting non-linearities, and provides predictive
uncertainty of competitive quality.
Quantization for Rapid Deployment of Deep Neural Networks
Authors: Jun Haeng Lee, Sangwon Ha, Saerom Choi, Won-Jo Lee, Seungwon Lee
Date: 2018-10-12 00:00:00
Proceeding: ICLR 2019 5
Tasks: object-detection, Object Detection, Quantization

This paper aims at rapid deployment of the state-of-the-art deep neural
networks (DNNs) to energy efficient accelerators without time-consuming fine
tuning or the availability of the full datasets. Converting DNNs in full
precision to limited precision is essential in taking advantage of the
accelerators with reduced memory footprint and computation power. However, such
a task is not trivial since it often requires the full training and validation
datasets for profiling the network statistics and fine tuning the networks to
recover the accuracy lost after quantization. To address these issues, we
propose a simple method recognizing channel-level distribution to reduce the
quantization-induced accuracy loss and minimize the required image samples for
profiling. We evaluated our method on eleven networks trained on the ImageNet
classification benchmark and a network trained on the Pascal VOC object
detection benchmark. The results prove that the networks can be quantized into
8-bit integer precision without fine tuning.
Training Deep Neural Network in Limited Precision
Authors: Hyunsun Park, Jun Haeng Lee, Youngmin Oh, Sangwon Ha, Seungwon Lee
Date: 2018-10-12 00:00:00

Energy and resource efficient training of DNNs will greatly extend the
applications of deep learning. However, there are three major obstacles which
mandate accurate calculation in high precision. In this paper, we tackle two of
them related to the loss of gradients during parameter update and
backpropagation through a softmax nonlinearity layer in low precision training.
We implemented SGD with Kahan summation by employing an additional parameter to
virtually extend the bit-width of the parameters for a reliable parameter
update. We also proposed a simple guideline to help select the appropriate
bit-width for the last FC layer followed by a softmax nonlinearity layer. It
determines the lower bound of the required bit-width based on the class size of
the dataset. Extensive experiments on various network architectures and
benchmarks verifies the effectiveness of the proposed technique for low
precision training.
Quantifying the amount of visual information used by neural caption generators
Authors: Marc Tanti, Albert Gatt, Kenneth P. Camilleri
Date: 2018-10-12 00:00:00
Tasks: Image Captioning, Position, Sensitivity

This paper addresses the sensitivity of neural image caption generators to
their visual input. A sensitivity analysis and omission analysis based on image
foils is reported, showing that the extent to which image captioning
architectures retain and are sensitive to visual information varies depending
on the type of word being generated and the position in the caption as a whole.
We motivate this work in the context of broader goals in the field to achieve
more explainability in AI.
Pre-gen metrics: Predicting caption quality metrics without generating captions
Authors: Marc Tanti, Albert Gatt, Adrian Muscat
Date: 2018-10-12 00:00:00
Tasks: Caption Generation

Image caption generation systems are typically evaluated against reference
outputs. We show that it is possible to predict output quality without
generating the captions, based on the probability assigned by the neural model
to the reference captions. Such pre-gen metrics are strongly correlated to
standard evaluation metrics.
Safe Grid Search with Optimal Complexity
Authors: Eugene Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi
Date: 2018-10-12 00:00:00

Popular machine learning estimators involve regularization parameters that can be challenging to tune, and standard strategies rely on grid search for this task. In this paper, we revisit the techniques of approximating the regularization path up to predefined tolerance $\epsilon$ in a unified framework and show that its complexity is $O(1/\sqrt[d]{\epsilon})$ for uniformly convex loss of order $d \geq 2$ and $O(1/\sqrt{\epsilon})$ for Generalized Self-Concordant functions. This framework encompasses least-squares but also logistic regression, a case that as far as we know was not handled as precisely in previous works. We leverage our technique to provide refined bounds on the validation error as well as a practical algorithm for hyperparameter tuning. The latter has global convergence guarantee when targeting a prescribed accuracy on the validation set. Last but not least, our approach helps relieving the practitioner from the (often neglected) task of selecting a stopping criterion when optimizing over the training set: our method automatically calibrates this criterion based on the targeted accuracy on the validation set.
Mode Normalization
Authors: Lucas Deecke, Iain Murray, Hakan Bilen
Date: 2018-10-12 00:00:00
Proceeding: ICLR 2019 5

Normalization methods are a central building block in the deep learning
toolbox. They accelerate and stabilize training, while decreasing the
dependence on manually tuned learning rate schedules. When learning from
multi-modal distributions, the effectiveness of batch normalization (BN),
arguably the most prominent normalization method, is reduced. As a remedy, we
propose a more flexible approach: by extending the normalization to more than a
single mean and variance, we detect modes of data on-the-fly, jointly
normalizing samples that share common features. We demonstrate that our method
outperforms BN and other widely used normalization techniques in several
experiments, including single and multi-task datasets.
Modeling Varying Camera-IMU Time Offset in Optimization-Based Visual-Inertial Odometry
Authors: Yonggen Ling, Linchao Bao, Zequn Jie, Fengming Zhu, Ziyang Li, Shanmin Tang, Yongsheng Liu, Wei Liu, Tong Zhang
Date: 2018-10-12 00:00:00
Proceeding: ECCV 2018 9

Combining cameras and inertial measurement units (IMUs) has been proven
effective in motion tracking, as these two sensing modalities offer
complementary characteristics that are suitable for fusion. While most works
focus on global-shutter cameras and synchronized sensor measurements,
consumer-grade devices are mostly equipped with rolling-shutter cameras and
suffer from imperfect sensor synchronization. In this work, we propose a
nonlinear optimization-based monocular visual inertial odometry (VIO) with
varying camera-IMU time offset modeled as an unknown variable. Our approach is
able to handle the rolling-shutter effects and imperfect sensor synchronization
in a unified way. Additionally, we introduce an efficient algorithm based on
dynamic programming and red-black tree to speed up IMU integration over
variable-length time intervals during the optimization. An uncertainty-aware
initialization is also presented to launch the VIO robustly. Comparisons with
state-of-the-art methods on the Euroc dataset and mobile phone data are shown
to validate the effectiveness of our approach.
Cats or CAT scans: transfer learning from natural or medical image source datasets?
Authors: Veronika Cheplygina
Date: 2018-10-12 00:00:00
Tasks: Diversity, Medical Image Analysis, Transfer Learning

Transfer learning is a widely used strategy in medical image analysis.
Instead of only training a network with a limited amount of data from the
target task of interest, we can first train the network with other, potentially
larger source datasets, creating a more robust model. The source datasets do
not have to be related to the target task. For a classification task in lung CT
images, we could use both head CT images, or images of cats, as the source.
While head CT images appear more similar to lung CT images, the number and
diversity of cat images might lead to a better model overall. In this survey we
review a number of papers that have performed similar comparisons. Although the
answer to which strategy is best seems to be "it depends", we discuss a number
of research directions we need to take as a community, to gain more
understanding of this topic.
An algebraic-geometric approach for linear regression without correspondences
Authors: Manolis C. Tsakiris, Liangzu Peng, Aldo Conca, Laurent Kneip, Yuanming Shi, Hayoung Choi
Date: 2018-10-12 00:00:00
Tasks: regression

Linear regression without correspondences is the problem of performing a linear regression fit to a dataset for which the correspondences between the independent samples and the observations are unknown. Such a problem naturally arises in diverse domains such as computer vision, data mining, communications and biology. In its simplest form, it is tantamount to solving a linear system of equations, for which the entries of the right hand side vector have been permuted. This type of data corruption renders the linear regression task considerably harder, even in the absence of other corruptions, such as noise, outliers or missing entries. Existing methods are either applicable only to noiseless data or they are very sensitive to initialization or they work only for partially shuffled data. In this paper we address these issues via an algebraic geometric approach, which uses symmetric polynomials to extract permutation-invariant constraints that the parameters $\xi^* \in \Re^n$ of the linear regression model must satisfy. This naturally leads to a polynomial system of $n$ equations in $n$ unknowns, which contains $\xi^*$ in its root locus. Using the machinery of algebraic geometry we prove that as long as the independent samples are generic, this polynomial system is always consistent with at most $n!$ complex roots, regardless of any type of corruption inflicted on the observations. The algorithmic implication of this fact is that one can always solve this polynomial system and use its most suitable root as initialization to the Expectation Maximization algorithm. To the best of our knowledge, the resulting method is the first working solution for small values of $n$ able to handle thousands of fully shuffled noisy observations in milliseconds.
MPTV: Matching Pursuit Based Total Variation Minimization for Image Deconvolution
Authors: Dong Gong, Mingkui Tan, Qinfeng Shi, Anton Van Den Hengel, Yanning Zhang
Date: 2018-10-12 00:00:00
Tasks: Image Deconvolution

Total variation (TV) regularization has proven effective for a range of
computer vision tasks through its preferential weighting of sharp image edges.
Existing TV-based methods, however, often suffer from the over-smoothing issue
and solution bias caused by the homogeneous penalization. In this paper, we
consider addressing these issues by applying inhomogeneous regularization on
different image components. We formulate the inhomogeneous TV minimization
problem as a convex quadratic constrained linear programming problem. Relying
on this new model, we propose a matching pursuit based total variation
minimization method (MPTV), specifically for image deconvolution. The proposed
MPTV method is essentially a cutting-plane method, which iteratively activates
a subset of nonzero image gradients, and then solves a subproblem focusing on
those activated gradients only. Compared to existing methods, MPTV is less
sensitive to the choice of the trade-off parameter between data fitting and
regularization. Moreover, the inhomogeneity of MPTV alleviates the
over-smoothing and ringing artifacts, and improves the robustness to errors in
blur kernel. Extensive experiments on different tasks demonstrate the
superiority of the proposed method over the current state-of-the-art.
HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of Documents
Authors: Hosein Azarbonyad, Mostafa Dehghani, Tom Kenter, Maarten Marx, Jaap Kamps, Maarten de Rijke
Date: 2018-10-12 00:00:00
Tasks: Diversity, Topic Models

A high degree of topical diversity is often considered to be an important
characteristic of interesting text documents. A recent proposal for measuring
topical diversity identifies three distributions for assessing the diversity of
documents: distributions of words within documents, words within topics, and
topics within documents. Topic models play a central role in this approach and,
hence, their quality is crucial to the efficacy of measuring topical diversity.
The quality of topic models is affected by two causes: generality and impurity
of topics. General topics only include common information of a background
corpus and are assigned to most of the documents. Impure topics contain words
that are not related to the topic. Impurity lowers the interpretability of
topic models. Impure topics are likely to get assigned to documents
erroneously. We propose a hierarchical re-estimation process aimed at removing
generality and impurity. Our approach has three re-estimation components: (1)
document re-estimation, which removes general words from the documents; (2)
topic re-estimation, which re-estimates the distribution over words of each
topic; and (3) topic assignment re-estimation, which re-estimates for each
document its distributions over topics. For measuring topical diversity of text
documents, our HiTR approach improves over the state-of-the-art measured on
PubMed dataset.
Real-time self-adaptive deep stereo
Authors: Alessio Tonioni, Fabio Tosi, Matteo Poggi, Stefano Mattoccia, Luigi Di Stefano
Date: 2018-10-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Stereo Depth Estimation

Deep convolutional neural networks trained end-to-end are the
state-of-the-art methods to regress dense disparity maps from stereo pairs.
These models, however, suffer from a notable decrease in accuracy when exposed
to scenarios significantly different from the training set, e.g., real vs
synthetic images, etc.). We argue that it is extremely unlikely to gather
enough samples to achieve effective training/tuning in any target domain, thus
making this setup impractical for many applications. Instead, we propose to
perform unsupervised and continuous online adaptation of a deep stereo network,
which allows for preserving its accuracy in any environment. However, this
strategy is extremely computationally demanding and thus prevents real-time
inference. We address this issue introducing a new lightweight, yet effective,
deep stereo architecture, Modularly ADaptive Network (MADNet) and developing a
Modular ADaptation (MAD) algorithm, which independently trains sub-portions of
the network. By deploying MADNet together with MAD we introduce the first
real-time self-adaptive deep stereo system enabling competitive performance on
heterogeneous datasets.
DeepScores and Deep Watershed Detection: current state and open issues
Authors: Ismail Elezi, Lukas Tuggener, Marcello Pelillo, Thilo Stadelmann
Date: 2018-10-12 00:00:00
Tasks: object-detection, Object Detection

This paper gives an overview of our current Optical Music Recognition (OMR)
research. We recently released the OMR dataset \emph{DeepScores} as well as the
object detection method \emph{Deep Watershed Detector}. We are currently taking
some additional steps to improve both of them. Here we summarize current and
future efforts, aimed at improving usefulness on real-world task and tackling
extreme class imbalance.
Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Data
Authors: Tim-Oliver Buchholz, Mareike Jordan, Gaia Pigino, Florian Jug
Date: 2018-10-12 00:00:00
Tasks: Cryogenic Electron Microscopy (cryo-EM), Denoising, Image Restoration

Multiple approaches to use deep learning for image restoration have recently
been proposed. Training such approaches requires well registered pairs of high
and low quality images. While this is easily achievable for many imaging
modalities, e.g. fluorescence light microscopy, for others it is not.
Cryo-transmission electron microscopy (cryo-TEM) could profoundly benefit from
improved denoising methods, unfortunately it is one of the latter. Here we show
how recent advances in network training for image restoration tasks, i.e.
denoising, can be applied to cryo-TEM data. We describe our proposed method and
show how it can be applied to single cryo-TEM projections and whole
cryo-tomographic image volumes. Our proposed restoration method dramatically
increases contrast in cryo-TEM images, which improves the interpretability of
the acquired data. Furthermore we show that automated downstream processing on
restored image data, demonstrated on a dense segmentation task, leads to
improved results.
A Gentle Introduction to Deep Learning in Medical Image Processing
Authors: Andreas Maier, Christopher Syben, Tobias Lasser, Christian Riess
Date: 2018-10-12 00:00:00
Tasks: Deep Learning, Image Registration, Image Segmentation, Semantic Segmentation

This paper tries to give a gentle introduction to deep learning in medical
image processing, proceeding from theoretical foundations to applications. We
first discuss general reasons for the popularity of deep learning, including
several major breakthroughs in computer science. Next, we start reviewing the
fundamental basics of the perceptron and neural networks, along with some
fundamental theory that is often omitted. Doing so allows us to understand the
reasons for the rise of deep learning in many application domains. Obviously
medical image processing is one of these areas which has been largely affected
by this rapid progress, in particular in image detection and recognition, image
segmentation, image registration, and computer-aided diagnosis. There are also
recent trends in physical simulation, modelling, and reconstruction that have
led to astonishing results. Yet, some of these approaches neglect prior
knowledge and hence bear the risk of producing implausible results. These
apparent weaknesses highlight current limitations of deep learning. However, we
also briefly discuss promising approaches that might be able to resolve these
problems in the future.
Thermal Infrared Colorization via Conditional Generative Adversarial Network
Authors: Xiaodong Kuang, Xiubao Sui, Chengwei Liu, Yu-An Liu, Qian Chen, Guohua Gu
Date: 2018-10-12 00:00:00
Tasks: Colorization, Generative Adversarial Network

Transforming a thermal infrared image into a realistic RGB image is a
challenging task. In this paper we propose a deep learning method to bridge
this gap. We propose learning the transformation mapping using a coarse-to-fine
generator that preserves the details. Since the standard mean squared loss
cannot penalize the distance between colorized and ground truth images well, we
propose a composite loss function that combines content, adversarial,
perceptual and total variation losses. The content loss is used to recover
global image information while the latter three losses are used to synthesize
local realistic textures. Quantitative and qualitative experiments demonstrate
that our approach significantly outperforms existing approaches.
Point Cloud Colorization Based on Densely Annotated 3D Shape Dataset
Authors: Xu Cao, Katashi Nagao
Date: 2018-10-12 00:00:00
Tasks: Colorization

This paper introduces DensePoint, a densely sampled and annotated point cloud
dataset containing over 10,000 single objects across 16 categories, by merging
different kind of information from two existing datasets. Each point cloud in
DensePoint contains 40,000 points, and each point is associated with two sorts
of information: RGB value and part annotation. In addition, we propose a method
for point cloud colorization by utilizing Generative Adversarial Networks
(GANs). The network makes it possible to generate colours for point clouds of
single objects by only giving the point cloud itself. Experiments on DensePoint
show that there exist clear boundaries in point clouds between different parts
of an object, suggesting that the proposed network is able to generate
reasonably good colours. Our dataset is publicly available on the project page.
Sequential Learning of Movement Prediction in Dynamic Environments using LSTM Autoencoder
Authors: Meenakshi Sarkar, Debasish Ghose
Date: 2018-10-12 00:00:00
Tasks: Decoder, Navigate, OpenAI Gym, Reinforcement Learning

Predicting movement of objects while the action of learning agent interacts
with the dynamics of the scene still remains a key challenge in robotics. We
propose a multi-layer Long Short Term Memory (LSTM) autoendocer network that
predicts future frames for a robot navigating in a dynamic environment with
moving obstacles. The autoencoder network is composed of a state and action
conditioned decoder network that reconstructs the future frames of video,
conditioned on the action taken by the agent. The input image frames are first
transformed into low dimensional feature vectors with a pre-trained encoder
network and then reconstructed with the LSTM autoencoder network to generate
the future frames. A virtual environment, based on the OpenAi-Gym framework for
robotics, is used to gather training data and test the proposed network. The
initial experiments show promising results indicating that these predicted
frames can be used by an appropriate reinforcement learning framework in future
to navigate around dynamic obstacles.