pwc-archive/papers-with-abstracts huggingface dataset:
Segmenting Potentially Cancerous Areas in Prostate Biopsies using Semi-Automatically Annotated Data
Authors: Nikolay Burlutskiy, Nicolas Pinchaud, Feng Gu, Daniel Hägg, Mats Andersson, Lars Björk, Kristian Eurén, Cristina Svensson, Lena Kajland Wilén, Martin Hedlund
Date: 2019-04-15 00:00:00
Tasks: whole slide images

Gleason grading specified in ISUP 2014 is the clinical standard in staging
prostate cancer and the most important part of the treatment decision. However,
the grading is subjective and suffers from high intra and inter-user
variability. To improve the consistency and objectivity in the grading, we
introduced glandular tissue WithOut Basal cells (WOB) as the ground truth. The
presence of basal cells is the most accepted biomarker for benign glandular
tissue and the absence of basal cells is a strong indicator of acinar prostatic
adenocarcinoma, the most common form of prostate cancer. Glandular tissue can
objectively be assessed as WOB or not WOB by using specific immunostaining for
glandular tissue (Cytokeratin 8/18) and for basal cells (Cytokeratin 5/6 +
p63). Even more, WOB allowed us to develop a semi-automated data generation
pipeline to speed up the tremendously time consuming and expensive process of
annotating whole slide images by pathologists. We generated 295 prostatectomy
images exhaustively annotated with WOB. Then we used our Deep Learning
Framework, which achieved the $2^{nd}$ best reported score in Camelyon17
Challenge, to train networks for segmenting WOB in needle biopsies. Evaluation
of the model on 63 needle biopsies showed promising results which were improved
further by finetuning the model on 118 biopsies annotated with WOB, achieving
F1-score of 0.80 and Precision-Recall AUC of 0.89 at the pixel-level. Then we
compared the performance of the model against 17 biopsies annotated
independently by 3 pathologists using only H\&E staining. The comparison
demonstrated that the model performed on a par with the pathologists. Finally,
the model detected and accurately outlined existing WOB areas in two biopsies
incorrectly annotated as totally WOB-free biopsies by three pathologists and in
one biopsy by two pathologists.
Influence of Control Parameters and the Size of Biomedical Image Datasets on the Success of Adversarial Attacks
Authors: Vassili Kovalev, Dmitry Voynov
Date: 2019-04-15 00:00:00

In this paper, we study dependence of the success rate of adversarial attacks
to the Deep Neural Networks on the biomedical image type, control parameters,
and image dataset size. With this work, we are going to contribute towards
accumulation of experimental results on adversarial attacks for the community
dealing with biomedical images. The white-box Projected Gradient Descent
attacks were examined based on 8 classification tasks and 13 image datasets
containing a total of 605,080 chest X-ray and 317,000 histology images of
malignant tumors. We concluded that: (1) An increase of the amplitude of
perturbation in generating malicious adversarial images leads to a growth of
the fraction of successful attacks for the majority of image types examined in
this study. (2) Histology images tend to be less sensitive to the growth of
amplitude of adversarial perturbations. (3) Percentage of successful attacks is
growing with an increase of the number of iterations of the algorithm of
generating adversarial perturbations with an asymptotic stabilization. (4) It
was found that the success of attacks dropping dramatically when the original
confidence of predicting image class exceeds 0.95. (5) The expected dependence
of the percentage of successful attacks on the size of image training set was
not confirmed.
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent
Authors: Karthik A. Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein
Date: 2019-04-15 00:00:00

This paper studies how neural network architecture affects the speed of training. We introduce a simple concept called gradient confusion to help formally analyze this. When gradient confusion is high, stochastic gradients produced by different data samples may be negatively correlated, slowing down convergence. But when gradient confusion is low, data samples interact harmoniously, and training proceeds quickly. Through theoretical and experimental results, we demonstrate how the neural network architecture affects gradient confusion, and thus the efficiency of training. Our results show that, for popular initialization techniques, increasing the width of neural networks leads to lower gradient confusion, and thus faster model training. On the other hand, increasing the depth of neural networks has the opposite effect. Our results indicate that alternate initialization techniques or networks using both batch normalization and skip connections help reduce the training burden of very deep networks.
On the Performance of Differential Evolution for Hyperparameter Tuning
Authors: Mischa Schmidt, Shahd Safarani, Julia Gastinger, Tobias Jacobs, Sebastien Nicolas, Anett Schülke
Date: 2019-04-15 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, SMAC, SMAC+

Automated hyperparameter tuning aspires to facilitate the application of
machine learning for non-experts. In the literature, different optimization
approaches are applied for that purpose. This paper investigates the
performance of Differential Evolution for tuning hyperparameters of supervised
learning algorithms for classification tasks. This empirical study involves a
range of different machine learning algorithms and datasets with various
characteristics to compare the performance of Differential Evolution with
Sequential Model-based Algorithm Configuration (SMAC), a reference Bayesian
Optimization approach. The results indicate that Differential Evolution
outperforms SMAC for most datasets when tuning a given machine learning
algorithm - particularly when breaking ties in a first-to-report fashion. Only
for the tightest of computational budgets SMAC performs better. On small
datasets, Differential Evolution outperforms SMAC by 19% (37% after
tie-breaking). In a second experiment across a range of representative datasets
taken from the literature, Differential Evolution scores 15% (23% after
tie-breaking) more wins than SMAC.
LeanResNet: A Low-cost Yet Effective Convolutional Residual Networks
Authors: Jonathan Ephrath, Lars Ruthotto, Eldad Haber, Eran Treister
Date: 2019-04-15 00:00:00
Tasks: General Classification, image-classification, Image Classification

Convolutional Neural Networks (CNNs) filter the input data using spatial convolution operators with compact stencils. Commonly, the convolution operators couple features from all channels, which leads to immense computational cost in the training of and prediction with CNNs. To improve the efficiency of CNNs, we introduce lean convolution operators that reduce the number of parameters and computational complexity, and can be used in a wide range of existing CNNs. Here, we exemplify their use in residual networks (ResNets), which have been very reliable for a few years now and analyzed intensively. In our experiments on three image classification problems, the proposed LeanResNet yields results that are comparable to other recently proposed reduced architectures using similar number of parameters.
Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice
Authors: Mayukh Das, Yang Yu, Devendra Singh Dhami, Gautam Kunapuli, Sriraam Natarajan
Date: 2019-04-15 00:00:00

Recently, deep models have been successfully applied in several applications,
especially with low-level representations. However, sparse, noisy samples and
structured domains (with multiple objects and interactions) are some of the
open challenges in most deep models. Column Networks, a deep architecture, can
succinctly capture such domain structure and interactions, but may still be
prone to sub-optimal learning from sparse and noisy samples. Inspired by the
success of human-advice guided learning in AI, especially in data-scarce
domains, we propose Knowledge-augmented Column Networks that leverage human
advice/knowledge for better learning with noisy/sparse samples. Our experiments
demonstrate that our approach leads to either superior overall performance or
faster convergence (i.e., both effective and efficient).
A framework for streamlined statistical prediction using topic models
Authors: Vanessa Glenny, Jonathan Tuke, Nigel Bean, Lewis Mitchell
Date: 2019-04-15 00:00:00
Proceeding: WS 2019 6
Tasks: Dimensionality Reduction, Topic Models

In the Humanities and Social Sciences, there is increasing interest in
approaches to information extraction, prediction, intelligent linkage, and
dimension reduction applicable to large text corpora. With approaches in these
fields being grounded in traditional statistical techniques, the need arises
for frameworks whereby advanced NLP techniques such as topic modelling may be
incorporated within classical methodologies. This paper provides a classical,
supervised, statistical learning framework for prediction from text, using
topic models as a data reduction method and the topics themselves as
predictors, alongside typical statistical tools for predictive modelling. We
apply this framework in a Social Sciences context (applied animal behaviour) as
well as a Humanities context (narrative analysis) as examples of this
framework. The results show that topic regression models perform comparably to
their much less efficient equivalents that use individual words as predictors.
Deep Comprehensive Correlation Mining for Image Clustering
Authors: Jianlong Wu, Keyu Long, Fei Wang, Chen Qian, Cheng Li, Zhouchen Lin, Hongbin Zha
Date: 2019-04-15 00:00:00
Proceeding: ICCV 2019 10
Tasks: Clustering, Deep Clustering, Image Clustering, Pseudo Label, Triplet

Recent developed deep unsupervised methods allow us to jointly learn representation and cluster unlabelled data. These deep clustering methods mainly focus on the correlation among samples, e.g., selecting high precision pairs to gradually tune the feature representation, which neglects other useful correlations. In this paper, we propose a novel clustering framework, named deep comprehensive correlation mining(DCCM), for exploring and taking full advantage of various kinds of correlations behind the unlabeled data from three aspects: 1) Instead of only using pair-wise information, pseudo-label supervision is proposed to investigate category information and learn discriminative features. 2) The features' robustness to image transformation of input space is fully explored, which benefits the network learning and significantly improves the performance. 3) The triplet mutual information among features is presented for clustering problem to lift the recently discovered instance-level deep mutual information to a triplet-level formation, which further helps to learn more discriminative features. Extensive experiments on several challenging datasets show that our method achieves good performance, e.g., attaining $62.3\%$ clustering accuracy on CIFAR-10, which is $10.1\%$ higher than the state-of-the-art results.
GraphTSNE: A Visualization Technique for Graph-Structured Data
Authors: Yao Yang Leow, Thomas Laurent, Xavier Bresson
Date: 2019-04-15 00:00:00
Tasks: Dimensionality Reduction

We present GraphTSNE, a novel visualization technique for graph-structured
data based on t-SNE. The growing interest in graph-structured data increases
the importance of gaining human insight into such datasets by means of
visualization. Among the most popular visualization techniques, classical t-SNE
is not suitable on such datasets because it has no mechanism to make use of
information from the graph structure. On the other hand, visualization
techniques which operate on graphs, such as Laplacian Eigenmaps and tsNET, have
no mechanism to make use of information from node features. Our proposed method
GraphTSNE produces visualizations which account for both graph structure and
node features. It is based on scalable and unsupervised training of a graph
convolutional network on a modified t-SNE loss. By assembling a suite of
evaluation metrics, we demonstrate that our method produces desirable
visualizations on three benchmark datasets.
Implicit Pairs for Boosting Unpaired Image-to-Image Translation
Authors: Yiftach Ginger, Dov Danon, Hadar Averbuch-Elor, Daniel Cohen-Or
Date: 2019-04-15 00:00:00
Tasks: Image-to-Image Translation, Translation

In image-to-image translation the goal is to learn a mapping from one image domain to another. In the case of supervised approaches the mapping is learned from paired samples. However, collecting large sets of image pairs is often either prohibitively expensive or not possible. As a result, in recent years more attention has been given to techniques that learn the mapping from unpaired sets. In our work, we show that injecting implicit pairs into unpaired sets strengthens the mapping between the two domains, improves the compatibility of their distributions, and leads to performance boosting of unsupervised techniques by over 14% across several measurements. The competence of the implicit pairs is further displayed with the use of pseudo-pairs, i.e., paired samples which only approximate a real pair. We demonstrate the effect of the approximated implicit samples on image-to-image translation problems, where such pseudo-pairs may be synthesized in one direction, but not in the other. We further show that pseudo-pairs are significantly more effective as implicit pairs in an unpaired setting, than directly using them explicitly in a paired setting.
Learning Deformable Kernels for Image and Video Denoising
Authors: Xiangyu Xu, Muchen Li, Wenxiu Sun
Date: 2019-04-15 00:00:00
Tasks: Denoising, Image Denoising, Video Denoising

Most of the classical denoising methods restore clear results by selecting
and averaging pixels in the noisy input. Instead of relying on hand-crafted
selecting and averaging strategies, we propose to explicitly learn this process
with deep neural networks. Specifically, we propose deformable 2D kernels for
image denoising where the sampling locations and kernel weights are both
learned. The proposed kernel naturally adapts to image structures and could
effectively reduce the oversmoothing artifacts. Furthermore, we develop 3D
deformable kernels for video denoising to more efficiently sample pixels across
the spatial-temporal space. Our method is able to solve the misalignment issues
of large motion from dynamic scenes. For better training our video denoising
model, we introduce the trilinear sampler and a new regularization term. We
demonstrate that the proposed method performs favorably against the
state-of-the-art image and video denoising approaches on both synthetic and
real-world data.
Exploiting Event Log Event Attributes in RNN Based Prediction
Authors: Markku Hinkka, Teemu Lehto, Keijo Heljanko
Date: 2019-04-15 00:00:00
Tasks: Attribute, Clustering, Prediction

In predictive process analytics, current and historical process data in event logs is used to predict the future, e.g., to predict the next activity or how long a process will still require to complete. Recurrent neural networks (RNN) and its subclasses have been demonstrated to be well suited for creating prediction models. Thus far, event attributes have not been fully utilized in these models. The biggest challenge in exploiting them in prediction models is the potentially large amount of event attributes and attribute values. We present a novel clustering technique that allows for trade-offs between prediction accuracy and the time needed for model training and prediction. As an additional finding, we also find that this clustering method combined with having raw event attribute values in some cases provides even better prediction accuracy at the cost of additional time required for training and prediction.
Algorithms used for the Cell Segmentation Benchmark Competition at ISBI 2019 by RWTH-GE
Authors: Dennis Eschweiler, Johannes Stegmaier
Date: 2019-04-15 00:00:00
Tasks: Cell Segmentation, Clustering, Segmentation

The presented algorithms for segmentation and tracking follow a 3-step
approach where we detect, track and finally segment nuclei. In the
preprocessing phase, we detect centroids of the cell nuclei using a
convolutional neural network (CNN) for the 2D images and a
Laplacian-of-Gaussian Scale Space Maximum Projection approach for the 3D data
sets. Tracking was performed in a backwards fashion on the predicted seed
points, i.e., starting at the last frame and sequentially connecting
corresponding objects until the first frame was reached. Correspondences were
identified by propagating detections of a frame t to its preceding frame t-1
and by combining redundant detections using a hierarchical clustering approach.
The tracked centroids were then used as input to variants of the seeded
watershed algorithm to obtain the final segmentation.
Disentangling Options with Hellinger Distance Regularizer
Authors: Minsung Hyun, Junyoung Choi, Nojun Kwak
Date: 2019-04-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In reinforcement learning (RL), temporal abstraction still remains as an
important and unsolved problem. The options framework provided clues to
temporal abstraction in the RL, and the option-critic architecture elegantly
solved the two problems of finding options and learning RL agents in an
end-to-end manner. However, it is necessary to examine whether the options
learned through this method play a mutually exclusive role. In this paper, we
propose a Hellinger distance regularizer, a method for disentangling options.
In addition, we will shed light on various indicators from the statistical
point of view to compare with the options learned through the existing
option-critic architecture.
DuBox: No-Prior Box Objection Detection via Residual Dual Scale Detectors
Authors: Shuai Chen, Jinpeng Li, Chuanqi Yao, Wenbo Hou, Shuo Qin, Wenyao Jin, Xu Tang
Date: 2019-04-15 00:00:00
Tasks: object-detection, Object Detection

Traditional neural objection detection methods use multi-scale features that
allow multiple detectors to perform detecting tasks independently and in
parallel. At the same time, with the handling of the prior box, the algorithm's
ability to deal with scale invariance is enhanced. However, too many prior
boxes and independent detectors will increase the computational redundancy of
the detection algorithm. In this study, we introduce Dubox, a new one-stage
approach that detects the objects without prior box. Working with multi-scale
features, the designed dual scale residual unit makes dual scale detectors no
longer run independently. The second scale detector learns the residual of the
first. Dubox has enhanced the capacity of heuristic-guided that can further
enable the first scale detector to maximize the detection of small targets and
the second to detect objects that cannot be identified by the first one.
Besides, for each scale detector, with the new classification-regression
progressive strapped loss makes our process not based on prior boxes.
Integrating these strategies, our detection algorithm has achieved excellent
performance in terms of speed and accuracy. Extensive experiments on the VOC,
COCO object detection benchmark have confirmed the effectiveness of this
algorithm.
Geometric Image Correspondence Verification by Dense Pixel Matching
Authors: Zakaria Laskar, Iaroslav Melekhov, Hamed R. -Tavakoli, Juha Ylioinas, Juho Kannala
Date: 2019-04-15 00:00:00
Tasks: Image Retrieval, Re-Ranking, Retrieval, Visual Localization

This paper addresses the problem of determining dense pixel correspondences between two images and its application to geometric correspondence verification in image retrieval. The main contribution is a geometric correspondence verification approach for re-ranking a shortlist of retrieved database images based on their dense pair-wise matching with the query image at a pixel level. We determine a set of cyclically consistent dense pixel matches between the pair of images and evaluate local similarity of matched pixels using neural network based image descriptors. Final re-ranking is based on a novel similarity function, which fuses the local similarity metric with a global similarity metric and a geometric consistency measure computed for the matched pixels. For dense matching our approach utilizes a modified version of a recently proposed dense geometric correspondence network (DGC-Net), which we also improve by optimizing the architecture. The proposed model and similarity metric compare favourably to the state-of-the-art image retrieval methods. In addition, we apply our method to the problem of long-term visual localization demonstrating promising results and generalization across datasets.
Improving interactive reinforcement learning: What makes a good teacher?
Authors: Francisco Cruz, Sven Magg, Yukie Nagai, Stefan Wermter
Date: 2019-04-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Interactive reinforcement learning has become an important apprenticeship
approach to speed up convergence in classic reinforcement learning problems. In
this regard, a variant of interactive reinforcement learning is policy shaping
which uses a parent-like trainer to propose the next action to be performed and
by doing so reduces the search space by advice. On some occasions, the trainer
may be another artificial agent which in turn was trained using reinforcement
learning methods to afterward becoming an advisor for other learner-agents. In
this work, we analyze internal representations and characteristics of
artificial agents to determine which agent may outperform others to become a
better trainer-agent. Using a polymath agent, as compared to a specialist
agent, an advisor leads to a larger reward and faster convergence of the reward
signal and also to a more stable behavior in terms of the state visit frequency
of the learner-agents. Moreover, we analyze system interaction parameters in
order to determine how influential they are in the apprenticeship process,
where the consistency of feedback is much more relevant when dealing with
different learner obedience parameters.
Singing voice synthesis based on convolutional neural networks
Authors: Kazuhiro Nakamura, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda
Date: 2019-04-15 00:00:00
Tasks: Singing Voice Synthesis

The present paper describes a singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized singing voices. In these systems, the relationship between musical score feature sequences and acoustic feature sequences extracted from singing voices is modeled by DNNs. Then, an acoustic feature sequence of an arbitrary musical score is output in units of frames by the trained DNNs, and a natural trajectory of a singing voice is obtained by using a parameter generation algorithm. As singing voices contain rich expression, a powerful technique to model them accurately is required. In the proposed technique, long-term dependencies of singing voices are modeled by CNNs. An acoustic feature sequence is generated in units of segments that consist of long-term frames, and a natural trajectory is obtained without the parameter generation algorithm. Experimental results in a subjective listening test show that the proposed architecture can synthesize natural sounding singing voices.