pwc-archive/papers-with-abstracts huggingface dataset:
Retinal OCT disease classification with variational autoencoder regularization
Authors: Max-Heinrich Laves, Sontje Ihler, Lüder A. Kahrs, Tobias Ortmaier
Date: 2019-03-23 00:00:00
Tasks: Classification, Clustering, Diagnostic, General Classification, Retinal OCT Disease Classification

According to the World Health Organization, 285 million people worldwide live
with visual impairment. The most commonly used imaging technique for diagnosis
in ophthalmology is optical coherence tomography (OCT). However, analysis of
retinal OCT requires trained ophthalmologists and time, making a comprehensive
early diagnosis unlikely. A recent study established a diagnostic tool based on
convolutional neural networks (CNN), which was trained on a large database of
retinal OCT images. The performance of the tool in classifying retinal
conditions was on par to that of trained medical experts. However, the training
of these networks is based on an enormous amount of labeled data, which is
expensive and difficult to obtain. Therefore, this paper describes a method
based on variational autoencoder regularization that improves classification
performance when using a limited amount of labeled data. This work uses a
two-path CNN model combining a classification network with an autoencoder (AE)
for regularization. The key idea behind this is to prevent overfitting when
using a limited training dataset size with small number of patients. Results
show superior classification performance compared to a pre-trained and fully
fine-tuned baseline ResNet-34. Clustering of the latent space in relation to
the disease class is distinct. Neural networks for disease classification on
OCTs can benefit from regularization using variational autoencoders when
trained with limited amount of patient data. Especially in the medical imaging
domain, data annotated by experts is expensive to obtain.
Cursive Overlapped Character Segmentation: An Enhanced Approach
Authors: Amjad Rehman
Date: 2019-03-23 00:00:00
Tasks: Handwriting Recognition, Segmentation

Segmentation of highly slanted and horizontally overlapped characters is a
challenging research area that is still fresh. Several techniques are reported
in the state of art, but produce low accuracy for the highly slanted characters
segmentation and cause overall low handwriting recognition precision.
Accordingly, this paper presents a simple yet effective approach for character
segmentation of such difficult slanted cursive words without using any slant
correction technique. Rather a new concept of core-zone is introduced for
segmenting such difficult slanted handwritten words. However, due to the
inherent nature of cursive words, few characters are over-segmented and
therefore, a threshold is selected heuristically to overcome this problem. For
fair comparison, difficult words are extracted from the IAM benchmark database.
Experiments thus performed exhibit promising result and high speed.
Understanding Unconventional Preprocessors in Deep Convolutional Neural Networks for Face Identification
Authors: Chollette C. Olisah, Lyndon Smith
Date: 2019-03-27 00:00:00
Tasks: Data Augmentation, Face Identification, Face Recognition, Quantization, Transfer Learning

Deep networks have achieved huge successes in application domains like object
and face recognition. The performance gain is attributed to different facets of
the network architecture such as: depth of the convolutional layers, activation
function, pooling, batch normalization, forward and back propagation and many
more. However, very little emphasis is made on the preprocessors. Therefore, in
this paper, the network's preprocessing module is varied across different
preprocessing approaches while keeping constant other facets of the network
architecture, to investigate the contribution preprocessing makes to the
network. Commonly used preprocessors are the data augmentation and
normalization and are termed conventional preprocessors. Others are termed the
unconventional preprocessors, they are: color space converters; HSV, CIE L*a*b*
and YCBCR, grey-level resolution preprocessors; full-based and plane-based
image quantization, illumination normalization and insensitive feature
preprocessing using: histogram equalization (HE), local contrast normalization
(LN) and complete face structural pattern (CFSP). To achieve fixed network
parameters, CNNs with transfer learning is employed. Knowledge from the
high-level feature vectors of the Inception-V3 network is transferred to
offline preprocessed LFW target data; and features trained using the SoftMax
classifier for face identification. The experiments show that the
discriminative capability of the deep networks can be improved by preprocessing
RGB data with HE, full-based and plane-based quantization, rgbGELog, and YCBCR,
preprocessors before feeding it to CNNs. However, for best performance, the
right setup of preprocessed data with augmentation and/or normalization is
required. The plane-based image quantization is found to increase the
homogeneity of neighborhood pixels and utilizes reduced bit depth for better
storage efficiency.
k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training
Authors: Yi-Lun Pan, Min-Jhih Huang, Kuo-Teng Ding, Ja-Ling Wu, Jyh-Shing Jang
Date: 2019-03-27 00:00:00
Tasks: De-identification, Generative Adversarial Network

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, "how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?" becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters.
DeepPoint3D: Learning Discriminative Local Descriptors using Deep Metric Learning on 3D Point Clouds
Authors: Siddharth Srivastava, Brejesh lall
Date: 2019-03-27 00:00:00
Tasks: Metric Learning

Learning local descriptors is an important problem in computer vision. While
there are many techniques for learning local patch descriptors for 2D images,
recently efforts have been made for learning local descriptors for 3D points.
The recent progress towards solving this problem in 3D leverages the strong
feature representation capability of image based convolutional neural networks
by utilizing RGB-D or multi-view representations. However, in this paper, we
propose to learn 3D local descriptors by directly processing unstructured 3D
point clouds without needing any intermediate representation. The method
constitutes a deep network for learning permutation invariant representation of
3D points. To learn the local descriptors, we use a multi-margin contrastive
loss which discriminates between similar and dissimilar points on a surface
while also leveraging the extent of dissimilarity among the negative samples at
the time of training. With comprehensive evaluation against strong baselines,
we show that the proposed method outperforms state-of-the-art methods for
matching points in 3D point clouds. Further, we demonstrate the effectiveness
of the proposed method on various applications achieving state-of-the-art
results.
COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation
Authors: Simone Bonechi, Paolo Andreini, Monica Bianchini, Franco Scarselli
Date: 2019-04-01 00:00:00
Tasks: Segmentation, Semantic Segmentation, Synthetic Data Generation, Text Detection, Text Segmentation, Weakly-supervised Learning

The absence of large scale datasets with pixel-level supervisions is a significant obstacle for the training of deep convolutional networks for scene text segmentation. For this reason, synthetic data generation is normally employed to enlarge the training dataset. Nonetheless, synthetic data cannot reproduce the complexity and variability of natural images. In this paper, a weakly supervised learning approach is used to reduce the shift between training on real and synthetic data. Pixel-level supervisions for a text detection dataset (i.e. where only bounding-box annotations are available) are generated. In particular, the COCO-Text-Segmentation (COCO_TS) dataset, which provides pixel-level supervisions for the COCO-Text dataset, is created and released. The generated annotations are used to train a deep convolutional neural network for semantic segmentation. Experiments show that the proposed dataset can be used instead of synthetic data, allowing us to use only a fraction of the training samples and significantly improving the performances.
Training Object Detectors on Synthetic Images Containing Reflecting Materials
Authors: Sebastian Hartwig, Timo Ropinski
Date: 2019-03-29 00:00:00
Tasks: Image Generation, Object

One of the grand challenges of deep learning is the requirement to obtain
large labeled training data sets. While synthesized data sets can be used to
overcome this challenge, it is important that these data sets close the reality
gap, i.e., a model trained on synthetic image data is able to generalize to
real images. Whereas, the reality gap can be considered bridged in several
application scenarios, training on synthesized images containing reflecting
materials requires further research. Since the appearance of objects with
reflecting materials is dominated by the surrounding environment, this
interaction needs to be considered during training data generation. Therefore,
within this paper we examine the effect of reflecting materials in the context
of synthetic image generation for training object detectors. We investigate the
influence of rendering approach used for image synthesis, the effect of domain
randomization, as well as the amount of used training data. To be able to
compare our results to the state-of-the-art, we focus on indoor scenes as they
have been investigated extensively. Within this scenario, bathroom furniture is
a natural choice for objects with reflecting materials, for which we report our
findings on real and synthetic testing data.
Depth-Aware Video Frame Interpolation
Authors: Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, Ming-Hsuan Yang
Date: 2019-04-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Optical Flow Estimation, Video Frame Interpolation

Video frame interpolation aims to synthesize nonexistent frames in-between
the original frames. While significant advances have been made from the recent
deep convolutional neural networks, the quality of interpolation is often
reduced due to large object motion or occlusion. In this work, we propose a
video frame interpolation method which explicitly detects the occlusion by
exploring the depth information. Specifically, we develop a depth-aware flow
projection layer to synthesize intermediate flows that preferably sample closer
objects than farther ones. In addition, we learn hierarchical features to
gather contextual information from neighboring pixels. The proposed model then
warps the input frames, depth maps, and contextual features based on the
optical flow and local interpolation kernels for synthesizing the output frame.
Our model is compact, efficient, and fully differentiable. Quantitative and
qualitative results demonstrate that the proposed model performs favorably
against state-of-the-art frame interpolation methods on a wide variety of
datasets.
DefectNET: multi-class fault detection on highly-imbalanced datasets
Authors: N. Anantrasirichai, David Bull
Date: 2019-04-01 00:00:00
Tasks: Defect Detection, Fault Detection, Semantic Segmentation

As a data-driven method, the performance of deep convolutional neural
networks (CNN) relies heavily on training data. The prediction results of
traditional networks give a bias toward larger classes, which tend to be the
background in the semantic segmentation task. This becomes a major problem for
fault detection, where the targets appear very small on the images and vary in
both types and sizes. In this paper we propose a new network architecture,
DefectNet, that offers multi-class (including but not limited to) defect
detection on highly-imbalanced datasets. DefectNet consists of two parallel
paths, which are a fully convolutional network and a dilated convolutional
network to detect large and small objects respectively. We propose a hybrid
loss maximising the usefulness of a dice loss and a cross entropy loss, and we
also employ the leaky rectified linear unit (ReLU) to deal with rare occurrence
of some targets in training batches. The prediction results show that our
DefectNet outperforms state-of-the-art networks for detecting multi-class
defects with the average accuracy improvement of approximately 10% on a wind
turbine.
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks
Authors: Aamir Mustafa, Salman Khan, Munawar Hayat, Roland Goecke, Jianbing Shen, Ling Shao
Date: 2019-04-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Adversarial Defense

Deep neural networks are vulnerable to adversarial attacks, which can fool them by adding minuscule perturbations to the input images. The robustness of existing defenses suffers greatly under white-box attack settings, where an adversary has full knowledge about the network and can iterate several times to find strong perturbations. We observe that the main reason for the existence of such perturbations is the close proximity of different class samples in the learned feature space. This allows model decisions to be totally changed by adding an imperceptible perturbation in the inputs. To counter this, we propose to class-wise disentangle the intermediate feature representations of deep networks. Specifically, we force the features for each class to lie inside a convex polytope that is maximally separated from the polytopes of other classes. In this manner, the network is forced to learn distinct and distant decision regions for each class. We observe that this simple constraint on the features greatly enhances the robustness of learned models, even against the strongest white-box attacks, without degrading the classification performance on clean images. We report extensive evaluations in both black-box and white-box attack scenarios and show significant gains in comparison to state-of-the art defenses.
Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters
Authors: Axel Barroso-Laguna, Edgar Riba, Daniel Ponsa, Krystian Mikolajczyk
Date: 2019-04-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Matching, Keypoint Detection

We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture. Handcrafted filters provide anchor structures for learned filters, which localize, score and rank repeatable features. Scale-space representation is used within the network to extract keypoints at different levels. We design a loss function to detect robust features that exist across a range of scales and to maximize the repeatability score. Our Key.Net model is trained on data synthetically created from ImageNet and evaluated on HPatches benchmark. Results show that our approach outperforms state-of-the-art detectors in terms of repeatability, matching performance and complexity.
Spherical U-Net on Cortical Surfaces: Methods and Applications
Authors: Fenqiang Zhao, Shunren Xia, Zhengwang Wu, Dingna Duan, Li Wang, Weili Lin, John H Gilmore, Dinggang Shen, Gang Li
Date: 2019-04-01 00:00:00
Tasks: Attribute, Computational Efficiency

Convolutional Neural Networks (CNNs) have been providing the state-of-the-art
performance for learning-related problems involving 2D/3D images in Euclidean
space. However, unlike in the Euclidean space, the shapes of many structures in
medical imaging have a spherical topology in a manifold space, e.g., brain
cortical or subcortical surfaces represented by triangular meshes, with large
inter-subject and intrasubject variations in vertex number and local
connectivity. Hence, there is no consistent neighborhood definition and thus no
straightforward convolution/transposed convolution operations for
cortical/subcortical surface data. In this paper, by leveraging the regular and
consistent geometric structure of the resampled cortical surface mapped onto
the spherical space, we propose a novel convolution filter analogous to the
standard convolution on the image grid. Accordingly, we develop corresponding
operations for convolution, pooling, and transposed convolution for spherical
surface data and thus construct spherical CNNs. Specifically, we propose the
Spherical U-Net architecture by replacing all operations in the standard U-Net
with their spherical operation counterparts. We then apply the Spherical U-Net
to two challenging and neuroscientifically important tasks in infant brains:
cortical surface parcellation and cortical attribute map development
prediction. Both applications demonstrate the competitive performance in the
accuracy, computational efficiency, and effectiveness of our proposed Spherical
U-Net, in comparison with the state-of-the-art methods.
Robustness of 3D Deep Learning in an Adversarial Setting
Authors: Matthew Wicker, Marta Kwiatkowska
Date: 2019-04-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Autonomous Navigation, Deep Learning

Understanding the spatial arrangement and nature of real-world objects is of
paramount importance to many complex engineering tasks, including autonomous
navigation. Deep learning has revolutionized state-of-the-art performance for
tasks in 3D environments; however, relatively little is known about the
robustness of these approaches in an adversarial setting. The lack of
comprehensive analysis makes it difficult to justify deployment of 3D deep
learning models in real-world, safety-critical applications. In this work, we
develop an algorithm for analysis of pointwise robustness of neural networks
that operate on 3D data. We show that current approaches presented for
understanding the resilience of state-of-the-art models vastly overestimate
their robustness. We then use our algorithm to evaluate an array of
state-of-the-art models in order to demonstrate their vulnerability to
occlusion attacks. We show that, in the worst case, these networks can be
reduced to 0% classification accuracy after the occlusion of at most 6.5% of
the occupied input space.
Early Diagnosis of Pneumonia with Deep Learning
Authors: Can Jozef Saul, Deniz Yagmur Urey, Can Doruk Taktakoglu
Date: 2019-04-01 00:00:00
Tasks: Deep Learning, General Classification

Pneumonia has been one of the fatal diseases and has the potential to result
in severe consequences within a short period of time, due to the flow of fluid
in lungs, which leads to drowning. If not acted upon by drugs at the right
time, pneumonia may result in death of individuals. Therefore, the early
diagnosis is a key factor along the progress of the disease. This paper focuses
on the biological progress of pneumonia and its detection by x-ray imaging,
overviews the studies conducted on enhancing the level of diagnosis, and
presents the methodology and results of an automation of xray images based on
various parameters in order to detect the disease at very early stages. In this
study we propose our deep learning architecture for the classification task,
which is trained with modified images, through multiple steps of preprocessing.
Our classification method uses convolutional neural networks and residual
network architecture for classifying the images. Our findings yield an accuracy
of 78.73%, surpassing the previously top scoring accuracy of 76.8%.
Automatic Nonrigid Histological Image Registration with Adaptive Multistep Algorithm
Authors: Marek Wodzinski, Andrzej Skalski
Date: 2019-04-01 00:00:00
Tasks: GPU, Image Registration

In this paper, we present a short description of the method proposed to ANHIR
challenge organized jointly with the IEEE ISBI 2019 conference. We propose a
method consisting of preprocessing, initial alignment, nonrigid registration
algorithms and a method to automatically choose the best result. The method
turned out to be robust (99.792% robustness) and accurate (0.38% average median
rTRE). The main drawback of the proposed method is relatively high computation
time. However, this aspect can be easily improved by cleaning the code and
proposing a GPU implementation.
Equivariant Multi-View Networks
Authors: Carlos Esteves, Yinshuang Xu, Christine Allen-Blanchette, Kostas Daniilidis
Date: 2019-04-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Shape Classification, 3D Shape Retrieval, Retrieval, Scene Classification

Several popular approaches to 3D vision tasks process multiple views of the input independently with deep neural networks pre-trained on natural images, achieving view permutation invariance through a single round of pooling over all views. We argue that this operation discards important information and leads to subpar global descriptors. In this paper, we propose a group convolutional approach to multiple view aggregation where convolutions are performed over a discrete subgroup of the rotation group, enabling, thus, joint reasoning over all views in an equivariant (instead of invariant) fashion, up to the very last layer. We further develop this idea to operate on smaller discrete homogeneous spaces of the rotation group, where a polar view representation is used to maintain equivariance with only a fraction of the number of input views. We set the new state of the art in several large scale 3D shape retrieval tasks, and show additional applications to panoramic scene classification.
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
Authors: Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman
Date: 2019-03-30 00:00:00
Proceeding: ICLR 2020 1
Tasks: Anomaly Detection, Decoder, Unsupervised Anomaly Detection, Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly

We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder maps the data into a latent space, from which the RSR layer extracts the subspace. The decoder then smoothly maps back the underlying subspace to a "manifold" close to the original inliers. Inliers and outliers are distinguished according to the distances between the original and mapped positions (small for inliers and large for outliers). Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall.