pwc-archive/papers-with-abstracts huggingface dataset:
Local Saddle Point Optimization: A Curvature Exploitation Approach
Authors: Leonard Adolphs, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann
Date: 2018-05-15 00:00:00

Gradient-based optimization methods are the most popular choice for finding
local optima for classical minimization and saddle point problems. Here, we
highlight a systemic issue of gradient dynamics that arise for saddle point
problems, namely the presence of undesired stable stationary points that are no
local optima. We propose a novel optimization approach that exploits curvature
information in order to escape from these undesired stationary points. We prove
that different optimization methods, including gradient method and Adagrad,
equipped with curvature exploitation can escape non-optimal stationary points.
We also provide empirical results on common saddle point problems which confirm
the advantage of using curvature exploitation.
Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification
Authors: Weijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, Jianbin Jiao
Date: 2017-11-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Generative Adversarial Network, Person Re-Identification, Translation, Unsupervised Domain Adaptation

Person re-identification (re-ID) models trained on one domain often fail to
generalize well to another. In our attempt, we present a "learning via
translation" framework. In the baseline, we translate the labeled images from
source to target domain in an unsupervised manner. We then train re-ID models
with the translated images by supervised methods. Yet, being an essential part
of this framework, unsupervised image-image translation suffers from the
information loss of source-domain labels during translation.
  Our motivation is two-fold. First, for each image, the discriminative cues
contained in its ID label should be maintained after translation. Second, given
the fact that two domains have entirely different persons, a translated image
should be dissimilar to any of the target IDs. To this end, we propose to
preserve two types of unsupervised similarities, 1) self-similarity of an image
before and after translation, and 2) domain-dissimilarity of a translated
source image and a target image. Both constraints are implemented in the
similarity preserving generative adversarial network (SPGAN) which consists of
an Siamese network and a CycleGAN. Through domain adaptation experiment, we
show that images generated by SPGAN are more suitable for domain adaptation and
yield consistent and competitive re-ID accuracy on two large-scale datasets.
Robust Adaptive Median Binary Pattern for noisy texture classification and retrieval
Authors: Mohammad Alkhatib, Adel Hafiane
Date: 2018-05-15 00:00:00
Tasks: Classification, General Classification, Retrieval, Texture Classification

Texture is an important cue for different computer vision tasks and
applications. Local Binary Pattern (LBP) is considered one of the best yet
efficient texture descriptors. However, LBP has some notable limitations,
mostly the sensitivity to noise. In this paper, we address these criteria by
introducing a novel texture descriptor, Robust Adaptive Median Binary Pattern
(RAMBP). RAMBP based on classification process of noisy pixels, adaptive
analysis window, scale analysis and image regions median comparison. The
proposed method handles images with high noisy textures, and increases the
discriminative properties by capturing microstructure and macrostructure
texture information. The proposed method has been evaluated on popular texture
datasets for classification and retrieval tasks, and under different high noise
conditions. Without any train or prior knowledge of noise type, RAMBP achieved
the best classification compared to state-of-the-art techniques. It scored more
than $90\%$ under $50\%$ impulse noise densities, more than $95\%$ under
Gaussian noised textures with standard deviation $\sigma = 5$, and more than
$99\%$ under Gaussian blurred textures with standard deviation $\sigma = 1.25$.
The proposed method yielded competitive results and high performance as one of
the best descriptors in noise-free texture classification. Furthermore, RAMBP
showed also high performance for the problem of noisy texture retrieval
providing high scores of recall and precision measures for textures with high
levels of noise.
Convolutional CRFs for Semantic Segmentation
Authors: Marvin T. T. Teichmann, Roberto Cipolla
Date: 2018-05-12 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

For the challenging semantic image segmentation task the most efficient
models have traditionally combined the structured modelling capabilities of
Conditional Random Fields (CRFs) with the feature extraction power of CNNs. In
more recent works however, CRF post-processing has fallen out of favour. We
argue that this is mainly due to the slow training and inference speeds of
CRFs, as well as the difficulty of learning the internal CRF parameters. To
overcome both issues we propose to add the assumption of conditional
independence to the framework of fully-connected CRFs. This allows us to
reformulate the inference in terms of convolutions, which can be implemented
highly efficiently on GPUs. Doing so speeds up inference and training by a
factor of more then 100. All parameters of the convolutional CRFs can easily be
optimized using backpropagation. To facilitating further CRF research we make
our implementation publicly available. Please visit:
https://github.com/MarvinTeichmann/ConvCRF
2sRanking-CNN: A 2-stage ranking-CNN for diagnosis of glaucoma from fundus images using CAM-extracted ROI as an intermediate input
Authors: Tae Joon Jun, Dohyeun Kim, Hoang Minh Nguyen, Daeyoung Kim, Youngsub Eom
Date: 2018-05-15 00:00:00
Tasks: Diagnostic

Glaucoma is a disease in which the optic nerve is chronically damaged by the
elevation of the intra-ocular pressure, resulting in visual field defect.
Therefore, it is important to monitor and treat suspected patients before they
are confirmed with glaucoma. In this paper, we propose a 2-stage ranking-CNN
that classifies fundus images as normal, suspicious, and glaucoma. Furthermore,
we propose a method of using the class activation map as a mask filter and
combining it with the original fundus image as an intermediate input. Our
results have improved the average accuracy by about 10% over the existing
3-class CNN and ranking-CNN, and especially improved the sensitivity of
suspicious class by more than 20% over 3-class CNN. In addition, the extracted
ROI was also found to overlap with the diagnostic criteria of the physician.
The method we propose is expected to be efficiently applied to any medical data
where there is a suspicious condition between normal and disease.
Learning Correlation Space for Time Series
Authors: Han Qiu, Hoang Thanh Lam, Francesco Fusco, Mathieu Sinn
Date: 2018-02-10 00:00:00
Tasks: Time Series, Time Series Analysis

We propose an approximation algorithm for efficient correlation search in
time series data. In our method, we use Fourier transform and neural network to
embed time series into a low-dimensional Euclidean space. The given space is
learned such that time series correlation can be effectively approximated from
Euclidean distance between corresponding embedded vectors. Therefore, search
for correlated time series can be done using an index in the embedding space
for efficient nearest neighbor search. Our theoretical analysis illustrates
that our method's accuracy can be guaranteed under certain regularity
conditions. We further conduct experiments on real-world datasets and the
results show that our method indeed outperforms the baseline solution. In
particular, for approximation of correlation, our method reduces the
approximation loss by a half in most test cases compared to the baseline
solution. For top-$k$ highest correlation search, our method improves the
precision from 5\% to 20\% while the query time is similar to the baseline
approach query time.
Practical Bayesian Optimization for Variable Cost Objectives
Authors: Mark McLeod, Michael A. Osborne, Stephen J. Roberts
Date: 2017-03-13 00:00:00
Tasks: Bayesian Optimization

We propose a novel Bayesian Optimization approach for black-box functions
with an environmental variable whose value determines the tradeoff between
evaluation cost and the fidelity of the evaluations. Further, we use a novel
approach to sampling support points, allowing faster construction of the
acquisition function. This allows us to achieve optimization with lower
overheads than previous approaches and is implemented for a more general class
of problem. We show this approach to be effective on synthetic and real world
benchmark problems.
Intrinsic dimension and its application to association rules
Authors: Tom Hanika, Friedrich Martin Schneider, Gerd Stumme
Date: 2018-05-15 00:00:00
Tasks: feature selection

The curse of dimensionality in the realm of association rules is twofold.
Firstly, we have the well known exponential increase in computational
complexity with increasing item set size. Secondly, there is a \emph{related
curse} concerned with the distribution of (spare) data itself in high
dimension. The former problem is often coped with by projection, i.e., feature
selection, whereas the best known strategy for the latter is avoidance. This
work summarizes the first attempt to provide a computationally feasible method
for measuring the extent of dimension curse present in a data set with respect
to a particular class machine of learning procedures. This recent development
enables the application of various other methods from geometric analysis to be
investigated and applied in machine learning procedures in the presence of high
dimension.
Exploring the Function Space of Deep-Learning Machines
Authors: Bo Li, David Saad
Date: 2017-08-04 00:00:00
Tasks: Deep Learning

The function space of deep-learning machines is investigated by studying
growth in the entropy of functions of a given error with respect to a reference
function, realized by a deep-learning machine. Using physics-inspired methods
we study both sparsely and densely-connected architectures to discover a
layer-wise convergence of candidate functions, marked by a corresponding
reduction in entropy when approaching the reference function, gain insight into
the importance of having a large number of layers, and observe phase
transitions as the error increases.
Multiple target tracking based on sets of trajectories
Authors: Ángel F. García-Fernández, Lennart Svensson, Mark R. Morelande
Date: 2016-05-26 00:00:00

We propose a solution of the multiple target tracking (MTT) problem based on sets of trajectories and the random finite set framework. A full Bayesian approach to MTT should characterise the distribution of the trajectories given the measurements, as it contains all information about the trajectories. We attain this by considering multi-object density functions in which objects are trajectories. For the standard tracking models, we also describe a conjugate family of multitrajectory density functions.
The Hierarchical Adaptive Forgetting Variational Filter
Authors: Vincent Moens
Date: 2018-05-15 00:00:00
Proceeding: ICML 2018 7
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

A common problem in Machine Learning and statistics consists in detecting
whether the current sample in a stream of data belongs to the same distribution
as previous ones, is an isolated outlier or inaugurates a new distribution of
data. We present a hierarchical Bayesian algorithm that aims at learning a
time-specific approximate posterior distribution of the parameters describing
the distribution of the data observed. We derive the update equations of the
variational parameters of the approximate posterior at each time step for
models from the exponential family, and show that these updates find
interesting correspondents in Reinforcement Learning (RL). In this perspective,
our model can be seen as a hierarchical RL algorithm that learns a posterior
distribution according to a certain stability confidence that is, in turn,
learned according to its own stability confidence. Finally, we show some
applications of our generic model, first in a RL context, next with an adaptive
Bayesian Autoregressive model, and finally in the context of Stochastic
Gradient Descent optimization.
Generating Continuous Representations of Medical Texts
Authors: Graham Spinks, Marie-Francine Moens
Date: 2018-05-15 00:00:00
Proceeding: NAACL 2018 6

We present an architecture that generates medical texts while learning an
informative, continuous representation with discriminative features. During
training the input to the system is a dataset of captions for medical X-Rays.
The acquired continuous representations are of particular interest for use in
many machine learning techniques where the discrete and high-dimensional nature
of textual input is an obstacle. We use an Adversarially Regularized
Autoencoder to create realistic text in both an unconditional and conditional
setting. We show that this technique is applicable to medical texts which often
contain syntactic and domain-specific shorthands. A quantitative evaluation
shows that we achieve a lower model perplexity than a traditional LSTM
generator.
Distribution-based Label Space Transformation for Multi-label Learning
Authors: Zongting Lyu, Yan Yan, Fei Wu
Date: 2018-05-15 00:00:00
Tasks: Dimensionality Reduction, Multi-Label Learning

Multi-label learning problems have manifested themselves in various machine
learning applications. The key to successful multi-label learning algorithms
lies in the exploration of inter-label correlations, which usually incur great
computational cost. Another notable factor in multi-label learning is that the
label vectors are usually extremely sparse, especially when the candidate label
vocabulary is very large and only a few instances are assigned to each
category. Recently, a label space transformation (LST) framework has been
proposed targeting these challenges. However, current methods based on LST
usually suffer from information loss in the label space dimension reduction
process and fail to address the sparsity problem effectively. In this paper, we
propose a distribution-based label space transformation (DLST) model. By
defining the distribution based on the similarity of label vectors, a more
comprehensive label structure can be captured. Then, by minimizing
KL-divergence of two distributions, the information of the original label space
can be approximately preserved in the latent space. Consequently, multi-label
classifier trained using the dense latent codes yields better performance. The
leverage of distribution enables DLST to fill out additional information about
the label correlations. This endows DLST the capability to handle label set
sparsity and training data sparsity in multi-label learning problems. With the
optimal latent code, a kernel logistic regression function is learned for the
mapping from feature space to the latent space. Then ML-KNN is employed to
recover the original label vector from the transformed latent code. Extensive
experiments on several benchmark datasets demonstrate that DLST not only
achieves high classification performance but also is computationally more
efficient.
Ocean Eddy Identification and Tracking using Neural Networks
Authors: Katharina Franz, Ribana Roscher, Andres Milioto, Susanne Wenzel, Jürgen Kusche
Date: 2018-03-20 00:00:00
Tasks: Object Tracking

Global climate change plays an essential role in our daily life. Mesoscale
ocean eddies have a significant impact on global warming, since they affect the
ocean dynamics, the energy as well as the mass transports of ocean circulation.
From satellite altimetry we can derive high-resolution, global maps containing
ocean signals with dominating coherent eddy structures. The aim of this study
is the development and evaluation of a deep-learning based approach for the
analysis of eddies. In detail, we develop an eddy identification and tracking
framework with two different approaches that are mainly based on feature
learning with convolutional neural networks. Furthermore, state-of-the-art
image processing tools and object tracking methods are used to support the eddy
tracking. In contrast to previous methods, our framework is able to learn a
representation of the data in which eddies can be detected and tracked in more
objective and robust way. We show the detection and tracking results on sea
level anomalies (SLA) data from the area of Australia and the East Australia
current, and compare our two eddy detection and tracking approaches to identify
the most robust and objective method.
Semantic Cluster Unary Loss for Efficient Deep Hashing
Authors: Shifeng Zhang, Jianmin Li, Bo Zhang
Date: 2018-05-15 00:00:00
Tasks: Deep Hashing, Information Retrieval, Retrieval, Triplet

Hashing method maps similar data to binary hashcodes with smaller hamming
distance, which has received a broad attention due to its low storage cost and
fast retrieval speed. With the rapid development of deep learning, deep hashing
methods have achieved promising results in efficient information retrieval.
Most of the existing deep hashing methods adopt pairwise or triplet losses to
deal with similarities underlying the data, but the training is difficult and
less efficient because $O(n^2)$ data pairs and $O(n^3)$ triplets are involved.
To address these issues, we propose a novel deep hashing algorithm with unary
loss which can be trained very efficiently. We first of all introduce a Unary
Upper Bound of the traditional triplet loss, thus reducing the complexity to
$O(n)$ and bridging the classification-based unary loss and the triplet loss.
Second, we propose a novel Semantic Cluster Deep Hashing (SCDH) algorithm by
introducing a modified Unary Upper Bound loss, named Semantic Cluster Unary
Loss (SCUL). The resultant hashcodes form several compact clusters, which means
hashcodes in the same cluster have similar semantic information. We also
demonstrate that the proposed SCDH is easy to be extended to semi-supervised
settings by incorporating the state-of-the-art semi-supervised learning
algorithms. Experiments on large-scale datasets show that the proposed method
is superior to state-of-the-art hashing algorithms.
A probabilistic framework for handwritten text line segmentation
Authors: Francisco Cruz, Oriol Ramos Terrades
Date: 2018-05-07 00:00:00

We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random
felds. This is a general method that can be applied to many computer vision
tasks. In this paper, we apply it to handwritten text line segmentation. We
conduct several experiments that demonstrate that our method deal with common
issues of this task, such as complex document layout or non-latin scripts. The
obtained results prove that our method achieve state-of-the-art performance on
different benchmark datasets without any particular fine tuning step.
Ro-SOS: Metric Expression Network (MEnet) for Robust Salient Object Segmentation
Authors: Delu Zeng, Yixuan He, Li Liu, Zhihong Chen, Jiabin Huang, Jie Chen, John Paisley
Date: 2018-05-15 00:00:00
Tasks: Saliency Detection, Semantic Segmentation

Although deep CNNs have brought significant improvement to image saliency detection, most CNN based models are sensitive to distortion such as compression and noise. In this paper, we propose an end-to-end generic salient object segmentation model called Metric Expression Network (MEnet) to deal with saliency detection with the tolerance of distortion. Within MEnet, a new topological metric space is constructed, whose implicit metric is determined by the deep network. As a result, we manage to group all the pixels in the observed image semantically within this latent space into two regions: a salient region and a non-salient region. With this architecture, all feature extractions are carried out at the pixel level, enabling fine granularity of output boundaries of the salient objects. What's more, we try to give a general analysis for the noise robustness of the network in the sense of Lipschitz and Jacobian literature. Experiments demonstrate that robust salient maps facilitating object segmentation can be generated by the proposed metric. Tests on several public benchmarks show that MEnet has achieved desirable performance. Furthermore, by direct computation and measuring the robustness, the proposed method outperforms previous CNN-based methods on distorted inputs.
A Deeply-Recursive Convolutional Network for Crowd Counting
Authors: Xinghao Ding, Zhirui Lin, Fujin He, Yu Wang, Yue Huang
Date: 2018-05-15 00:00:00
Tasks: Crowd Counting

The estimation of crowd count in images has a wide range of applications such
as video surveillance, traffic monitoring, public safety and urban planning.
Recently, the convolutional neural network (CNN) based approaches have been
shown to be more effective in crowd counting than traditional methods that use
handcrafted features. However, the existing CNN-based methods still suffer from
large number of parameters and large storage space, which require high storage
and computing resources and thus limit the real-world application.
Consequently, we propose a deeply-recursive network (DR-ResNet) based on ResNet
blocks for crowd counting. The recursive structure makes the network deeper
while keeping the number of parameters unchanged, which enhances network
capability to capture statistical regularities in the context of the crowd.
Besides, we generate a new dataset from the video-monitoring data of Beijing
bus station. Experimental results have demonstrated that proposed method
outperforms most state-of-the-art methods with far less number of parameters.
Stories for Images-in-Sequence by using Visual and Narrative Components
Authors: Marko Smilevski, Ilija Lalkovski, Gjorgji Madjarov
Date: 2018-05-15 00:00:00
Tasks: Sentence

Recent research in AI is focusing towards generating narrative stories about
visual scenes. It has the potential to achieve more human-like understanding
than just basic description generation of images- in-sequence. In this work, we
propose a solution for generating stories for images-in-sequence that is based
on the Sequence to Sequence model. As a novelty, our encoder model is composed
of two separate encoders, one that models the behaviour of the image sequence
and other that models the sentence-story generated for the previous image in
the sequence of images. By using the image sequence encoder we capture the
temporal dependencies between the image sequence and the sentence-story and by
using the previous sentence-story encoder we achieve a better story flow. Our
solution generates long human-like stories that not only describe the visual
context of the image sequence but also contains narrative and evaluative
language. The obtained results were confirmed by manual human evaluation.