pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Part Mining for Fine-grained Image Classification
Authors: Runsheng Zhang, Jian Zhang, Yaping Huang, Qi Zou
Date: 2019-02-26 00:00:00
Tasks: Classification, Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Object

Fine-grained image classification remains challenging due to the large intra-class variance and small inter-class variance. Since the subtle visual differences are only in local regions of discriminative parts among subcategories, part localization is a key issue for fine-grained image classification. Most existing approaches localize object or parts in an image with object or part annotations, which are expensive and labor-consuming. To tackle this issue, we propose a fully unsupervised part mining (UPM) approach to localize the discriminative parts without even image-level annotations, which largely improves the fine-grained classification performance. We first utilize pattern mining techniques to discover frequent patterns, i.e., co-occurrence highlighted regions, in the feature maps extracted from a pre-trained convolutional neural network (CNN) model. Inspired by the fact that these relevant meaningful patterns typically hold appearance and spatial consistency, we then cluster the mined regions to obtain the cluster centers and the discriminative parts surrounding the cluster centers are generated. Importantly, any annotations and sophisticated training procedures are not used in our proposed part localization approach. Finally, a multi-stream classification network is built for aggregating the original, object-level and part-level features simultaneously. Compared with other state-of-the-art approaches, our UPM approach achieves the competitive performance.
A Feature Selection Based on Perturbation Theory
Authors: Javad Rahimipour Anaraki, Hamid Usefi
Date: 2019-02-26 00:00:00
Tasks: feature selection

Consider a supervised dataset $D=[A\mid \textbf{b}]$, where $\textbf{b}$ is
the outcome column, rows of $D$ correspond to observations, and columns of $A$
are the features of the dataset. A central problem in machine learning and
pattern recognition is to select the most important features from $D$ to be
able to predict the outcome. In this paper, we provide a new feature selection
method where we use perturbation theory to detect correlations between
features. We solve $AX=\textbf{b}$ using the method of least squares and
singular value decomposition of $A$. In practical applications, such as in
bioinformatics, the number of rows of $A$ (observations) are much less than the
number of columns of $A$ (features). So we are dealing with singular matrices
with big condition numbers. Although it is known that the solutions of least
square problems in singular case are very sensitive to perturbations in $A$,
our novel approach in this paper is to prove that the correlations between
features can be detected by applying perturbations to $A$. The effectiveness of
our method is verified by performing a series of comparisons with conventional
and novel feature selection methods in the literature. It is demonstrated that
in most situations, our method chooses considerably less number of features
while attaining or exceeding the accuracy of the other methods.
Learning Vertex Convolutional Networks for Graph Classification
Authors: Lu Bai, Lixin Cui, Shu Wu, Yuhang Jiao, Edwin R. Hancock
Date: 2019-02-26 00:00:00
Tasks: Classification, General Classification, Graph Classification

In this paper, we develop a new aligned vertex convolutional network model to
learn multi-scale local-level vertex features for graph classification. Our
idea is to transform the graphs of arbitrary sizes into fixed-sized aligned
vertex grid structures, and define a new vertex convolution operation by
adopting a set of fixed-sized one-dimensional convolution filters on the grid
structure. We show that the proposed model not only integrates the precise
structural correspondence information between graphs but also minimises the
loss of structural information residing on local-level vertices. Experiments on
standard graph datasets demonstrate the effectiveness of the proposed model.
A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images
Authors: Jiahang Xu, Fangyang Jiao, Yechong Huang, Xinzhe Luo, Qian Xu, Ling Li, Xueling Liu, Chuantao Zuo, Ping Wu, Xiahai Zhuang
Date: 2019-02-26 00:00:00
Tasks: Diagnostic, General Classification, Segmentation

Background: Parkinson's disease (PD) is a prevalent long-term
neurodegenerative disease. Though the diagnostic criteria of PD are relatively
well defined, the current medical imaging diagnostic procedures are
expertise-demanding, and thus call for a higher-integrated AI-based diagnostic
algorithm. Methods: In this paper, we proposed an automatic, end-to-end,
multi-modality diagnosis framework, including segmentation, registration,
feature generation and machine learning, to process the information of the
striatum for the diagnosis of PD. Multiple modalities, including T1- weighted
MRI and 11C-CFT PET, were used in the proposed framework. The reliability of
this framework was then validated on a dataset from the PET center of Huashan
Hospital, as the dataset contains paired T1-MRI and CFT-PET images of 18 Normal
(NL) subjects and 49 PD subjects. Results: We obtained an accuracy of 100% for
the PD/NL classification task, besides, we conducted several comparative
experiments to validate the diagnosis ability of our framework. Conclusion:
Through experiment we illustrate that (1) automatic segmentation has the same
classification effect as the manual segmentation, (2) the multi-modality images
generates a better prediction than single modality images, and (3) volume
feature is shown to be irrelevant to PD diagnosis.
IF-TTN: Information Fused Temporal Transformation Network for Video Action Recognition
Authors: Ke Yang, Peng Qiao, Dongsheng Li, Yong Dou
Date: 2019-02-26 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

Effective spatiotemporal feature representation is crucial to the video-based
action recognition task. Focusing on discriminate spatiotemporal feature
learning, we propose Information Fused Temporal Transformation Network (IF-TTN)
for action recognition on top of popular Temporal Segment Network (TSN)
framework. In the network, Information Fusion Module (IFM) is designed to fuse
the appearance and motion features at multiple ConvNet levels for each video
snippet, forming a short-term video descriptor. With fused features as inputs,
Temporal Transformation Networks (TTN) are employed to model middle-term
temporal transformation between the neighboring snippets following a sequential
order. As TSN itself depicts long-term temporal structure by segmental
consensus, the proposed network comprehensively considers multiple granularity
temporal features. Our IF-TTN achieves the state-of-the-art results on two most
popular action recognition datasets: UCF101 and HMDB51. Empirical investigation
reveals that our architecture is robust to the input motion map quality.
Replacing optical flow with the motion vectors from compressed video stream,
the performance is still comparable to the flow-based methods while the testing
speed is 10x faster.
Effect Inference from Two-Group Data with Sampling Bias
Authors: Dave Zachariah, Petre Stoica
Date: 2019-02-26 00:00:00
Tasks: Vocal Bursts Valence Prediction

In many applications, different populations are compared using data that are sampled in a biased manner. Under sampling biases, standard methods that estimate the difference between the population means yield unreliable inferences. Here we develop an inference method that is resilient to sampling biases and is able to control the false positive errors under moderate bias levels in contrast to the standard approach. We demonstrate the method using synthetic and real biomarker data.
Efficient online learning with kernels for adversarial large scale problems
Authors: Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
Date: 2019-02-26 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: regression

We are interested in a framework of online learning with kernels for low-dimensional but large-scale and potentially adversarial datasets. We study the computational and theoretical performance of online variations of kernel Ridge regression. Despite its simplicity, the algorithm we study is the first to achieve the optimal regret for a wide range of kernels with a per-round complexity of order $n^\alpha$ with $\alpha < 2$. The algorithm we consider is based on approximating the kernel with the linear span of basis functions. Our contributions is two-fold: 1) For the Gaussian kernel, we propose to build the basis beforehand (independently of the data) through Taylor expansion. For $d$-dimensional inputs, we provide a (close to) optimal regret of order $O((\log n)^{d+1})$ with per-round time complexity and space complexity $O((\log n)^{2d})$. This makes the algorithm a suitable choice as soon as $n \gg e^d$ which is likely to happen in a scenario with small dimensional and large-scale dataset; 2) For general kernels with low effective dimension, the basis functions are updated sequentially in a data-adaptive fashion by sampling Nystr{\"o}m points. In this case, our algorithm improves the computational trade-off known for online kernel regression.
HexaGAN: Generative Adversarial Nets for Real World Classification
Authors: Uiwon Hwang, Dahuin Jung, Sungroh Yoon
Date: 2019-02-26 00:00:00
Tasks: Classification, General Classification, Generative Adversarial Network, Imputation

Most deep learning classification studies assume clean data. However, when dealing with the real world data, we encounter three problems such as 1) missing data, 2) class imbalance, and 3) missing label problems. These problems undermine the performance of a classifier. Various preprocessing techniques have been proposed to mitigate one of these problems, but an algorithm that assumes and resolves all three problems together has not been proposed yet. In this paper, we propose HexaGAN, a generative adversarial network framework that shows promising classification performance for all three problems. We interpret the three problems from a single perspective to solve them jointly. To enable this, the framework consists of six components, which interact with each other. We also devise novel loss functions corresponding to the architecture. The designed loss functions allow us to achieve state-of-the-art imputation performance, with up to a 14% improvement, and to generate high-quality class-conditional data. We evaluate the classification performance (F1-score) of the proposed method with 20% missingness and confirm up to a 5% improvement in comparison with the performance of combinations of state-of-the-art methods.
Region Deformer Networks for Unsupervised Depth Estimation from Unconstrained Monocular Videos
Authors: Haofei Xu, Jianmin Zheng, Jianfei Cai, Juyong Zhang
Date: 2019-02-26 00:00:00
Tasks: Depth Estimation

While learning based depth estimation from images/videos has achieved substantial progress, there still exist intrinsic limitations. Supervised methods are limited by a small amount of ground truth or labeled data and unsupervised methods for monocular videos are mostly based on the static scene assumption, not performing well on real world scenarios with the presence of dynamic objects. In this paper, we propose a new learning based method consisting of DepthNet, PoseNet and Region Deformer Networks (RDN) to estimate depth from unconstrained monocular videos without ground truth supervision. The core contribution lies in RDN for proper handling of rigid and non-rigid motions of various objects such as rigidly moving cars and deformable humans. In particular, a deformation based motion representation is proposed to model individual object motion on 2D images. This representation enables our method to be applicable to diverse unconstrained monocular videos. Our method can not only achieve the state-of-the-art results on standard benchmarks KITTI and Cityscapes, but also show promising results on a crowded pedestrian tracking dataset, which demonstrates the effectiveness of the deformation based motion representation. Code and trained models are available at https://github.com/haofeixu/rdn4depth.
Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network
Authors: Yechong Huang, Jiahang Xu, Yuncheng Zhou, Tong Tong, Xiahai Zhuang, the Alzheimer's Disease Neuroimaging Initiative
Date: 2019-02-26 00:00:00
Tasks: Diagnostic, image-classification, Image Classification, Prognosis

Alzheimer's Disease (AD) is one of the most concerned neurodegenerative
diseases. In the last decade, studies on AD diagnosis attached great
significance to artificial intelligence (AI)-based diagnostic algorithms. Among
the diverse modality imaging data, T1-weighted MRI and 18F-FDGPET are widely
researched for this task. In this paper, we propose a novel convolutional
neural network (CNN) to fuse the multi-modality information including T1-MRI
and FDG-PDT images around the hippocampal area for the diagnosis of AD.
Different from the traditional machine learning algorithms, this method does
not require manually extracted features, and utilizes the stateof-art 3D
image-processing CNNs to learn features for the diagnosis and prognosis of AD.
To validate the performance of the proposed network, we trained the classifier
with paired T1-MRI and FDG-PET images using the ADNI datasets, including 731
Normal (NL) subjects, 647 AD subjects, 441 stable MCI (sMCI) subjects and 326
progressive MCI (pMCI) subjects. We obtained the maximal accuracies of 90.10%
for NL/AD task, 87.46% for NL/pMCI task, and 76.90% for sMCI/pMCI task. The
proposed framework yields comparative results against state-of-the-art
approaches. Moreover, the experimental results have demonstrated that (1)
segmentation is not a prerequisite by using CNN, (2) the hippocampal area
provides enough information to give a reference to AD diagnosis. Keywords:
Alzheimer's Disease, Multi-modality, Image Classification, CNN, Deep Learning,
Hippocampal
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation
Authors: Antreas Antoniou, Amos Storkey
Date: 2019-02-26 00:00:00
Tasks: Data Augmentation, Few-Shot Learning, Meta-Learning, Unsupervised Few-Shot Image Classification, Unsupervised Few-Shot Learning

The field of few-shot learning has been laboriously explored in the
supervised setting, where per-class labels are available. On the other hand,
the unsupervised few-shot learning setting, where no labels of any kind are
required, has seen little investigation. We propose a method, named Assume,
Augment and Learn or AAL, for generating few-shot tasks using unlabeled data.
We randomly label a random subset of images from an unlabeled dataset to
generate a support set. Then by applying data augmentation on the support set's
images, and reusing the support set's labels, we obtain a target set. The
resulting few-shot tasks can be used to train any standard meta-learning
framework. Once trained, such a model, can be directly applied on small
real-labeled datasets without any changes or fine-tuning required. In our
experiments, the learned models achieve good generalization performance in a
variety of established few-shot learning tasks on Omniglot and Mini-Imagenet.
MC-ISTA-Net: Adaptive Measurement and Initialization and Channel Attention Optimization inspired Neural Network for Compressive Sensing
Authors: Nanyu Li, Cuiyin Liu
Date: 2019-02-26 00:00:00
Tasks: Compressive Sensing

The optimization inspired network can bridge convex optimization and neural networks in Compressive Sensing (CS) reconstruction of natural image, like ISTA-Net+, which mapping optimization algorithm: iterative shrinkage-thresholding algorithm (ISTA) into network. However, measurement matrix and input initialization are still hand-crafted, and multi-channel feature map contain information at different frequencies, which is treated equally across channels, hindering the ability of CS reconstruction in optimization-inspired networks. In order to solve the above problems, we proposed MC-ISTA-Net
Improving a tf-idf weighted document vector embedding
Authors: Craig W. Schmidt
Date: 2019-02-26 00:00:00

We examine a number of methods to compute a dense vector embedding for a
document in a corpus, given a set of word vectors such as those from word2vec
or GloVe. We describe two methods that can improve upon a simple weighted sum,
that are optimal in the sense that they maximizes a particular weighted cosine
similarity measure.
  We consider several weighting functions, including inverse document frequency
(idf), smooth inverse frequency (SIF), and the sub-sampling function used in
word2vec. We find that idf works best for our applications. We also use common
component removal proposed by Arora et al. as a post-process and find it is
helpful in most cases.
  We compare these embeddings variations to the doc2vec embedding on a new
evaluation task using TripAdvisor reviews, and also on the CQADupStack
benchmark from the literature.
Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification
Authors: Jianlin Li, Pengfei Yang, Jiangchao Liu, Liqian Chen, Xiaowei Huang, Lijun Zhang
Date: 2019-02-26 00:00:00

Deep neural networks (DNNs) have been shown lack of robustness for the vulnerability of their classification to small perturbations on the inputs. This has led to safety concerns of applying DNNs to safety-critical domains. Several verification approaches have been developed to automatically prove or disprove safety properties of DNNs. However, these approaches suffer from either the scalability problem, i.e., only small DNNs can be handled, or the precision problem, i.e., the obtained bounds are loose. This paper improves on a recent proposal of analyzing DNNs through the classic abstract interpretation technique, by a novel symbolic propagation technique. More specifically, the values of neurons are represented symbolically and propagated forwardly from the input layer to the output layer, on top of abstract domains. We show that our approach can achieve significantly higher precision and thus can prove more properties than using only abstract domains. Moreover, we show that the bounds derived from our approach on the hidden neurons, when applied to a state-of-the-art SMT based verification tool, can improve its performance. We implement our approach into a software tool and validate it over a few DNNs trained on benchmark datasets such as MNIST, etc.
Spiking Neural Network based Region Proposal Networks for Neuromorphic Vision Sensors
Authors: Jyotibdha Acharya, Vandana Padala, Arindam Basu
Date: 2019-02-26 00:00:00
Tasks: Clustering, Region Proposal

This paper presents a three layer spiking neural network based region
proposal network operating on data generated by neuromorphic vision sensors.
The proposed architecture consists of refractory, convolution and clustering
layers designed with bio-realistic leaky integrate and fire (LIF) neurons and
synapses. The proposed algorithm is tested on traffic scene recordings from a
DAVIS sensor setup. The performance of the region proposal network has been
compared with event based mean shift algorithm and is found to be far superior
(~50% better) in recall for similar precision (~85%). Computational and memory
complexity of the proposed method are also shown to be similar to that of event
based mean shift
Variational Multi-Phase Segmentation using High-Dimensional Local Features
Authors: Niklas Mevenkamp, Benjamin Berkels
Date: 2019-02-26 00:00:00
Tasks: Segmentation, Vocal Bursts Intensity Prediction

We propose a novel method for multi-phase segmentation of images based on
high-dimensional local feature vectors. While the method was developed for the
segmentation of extremely noisy crystal images based on localized Fourier
transforms, the resulting framework is not tied to specific feature
descriptors. For instance, using local spectral histograms as features, it
allows for robust texture segmentation. The segmentation itself is based on the
multi-phase Mumford-Shah model. Initializing the high-dimensional mean features
directly is computationally too demanding and ill-posed in practice. This is
resolved by projecting the features onto a low-dimensional space using
principle component analysis. The resulting objective functional is minimized
using a convexification and the Chambolle-Pock algorithm. Numerical results are
presented, illustrating that the algorithm is very competitive in texture
segmentation with state-of-the-art performance on the Prague benchmark and
provides new possibilities in crystal segmentation, being robust to extreme
noise and requiring no prior knowledge of the crystal structure.
Context Vectors are Reflections of Word Vectors in Half the Dimensions
Authors: Zhenisbek Assylbekov, Rustem Takhanov
Date: 2019-02-26 00:00:00
Tasks: Text Generation, Word Embeddings

This paper takes a step towards theoretical analysis of the relationship
between word embeddings and context embeddings in models such as word2vec. We
start from basic probabilistic assumptions on the nature of word vectors,
context vectors, and text generation. These assumptions are well supported
either empirically or theoretically by the existing literature. Next, we show
that under these assumptions the widely-used word-word PMI matrix is
approximately a random symmetric Gaussian ensemble. This, in turn, implies that
context vectors are reflections of word vectors in approximately half the
dimensions. As a direct application of our result, we suggest a theoretically
grounded way of tying weights in the SGNS model.
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images
Authors: Changhee Han, Kohei Murao, Tomoyuki Noguchi, Yusuke Kawata, Fumiya Uchiyama, Leonardo Rundo, Hideki Nakayama, Shin'ichi Satoh
Date: 2019-02-26 00:00:00
Tasks: Data Augmentation

Accurate Computer-Assisted Diagnosis, associated with proper data wrangling, can alleviate the risk of overlooking the diagnosis in a clinical environment. Towards this, as a Data Augmentation (DA) technique, Generative Adversarial Networks (GANs) can synthesize additional training data to handle the small/fragmented medical imaging datasets collected from various scanners; those images are realistic but completely different from the original ones, filling the data lack in the real image distribution. However, we cannot easily use them to locate disease areas, considering expert physicians' expensive annotation cost. Therefore, this paper proposes Conditional Progressive Growing of GANs (CPGGANs), incorporating highly-rough bounding box conditions incrementally into PGGANs to place brain metastases at desired positions/sizes on 256 X 256 Magnetic Resonance (MR) images, for Convolutional Neural Network-based tumor detection; this first GAN-based medical DA using automatic bounding box annotation improves the training robustness. The results show that CPGGAN-based DA can boost 10% sensitivity in diagnosis with clinically acceptable additional False Positives. Surprisingly, further tumor realism, achieved with additional normal brain MR images for CPGGAN training, does not contribute to detection performance, while even three physicians cannot accurately distinguish them from the real ones in Visual Turing Test.