January 26, 2020

3425 words 17 mins read

Paper Group ANR 1475

Gait recognition via deep learning of the center-of-pressure trajectory. Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers. A Unified Deep Learning Approach for Prediction of Parkinson’s Disease. A sparse resultant based method for efficient minimal solvers. Deep Neural Network Fingerprinting by Conferrable Adversarial Exam …

Gait recognition via deep learning of the center-of-pressure trajectory


Title	Gait recognition via deep learning of the center-of-pressure trajectory
Authors	Philippe Terrier
Abstract	The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure trajectory is sufficiently unique to identify a person with a high certainty. Thirty-six adults walked on a treadmill equipped with a force platform that recorded the positions of the center of pressure. The raw two-dimensional signals were sliced into segments of two gait cycles. A set of 20,250 segments from 30 subjects was used to configure and train convolutional neural networks (CNNs). The best CNN classified a separate set containing 2,250 segments with 99.9% overall accuracy. A second set of 4,500 segments from the six remaining subjects was then used for transfer learning. Several small subsamples of this set were selected randomly and used for fine tuning. Training with two segments per subject was sufficient to achieve 100% accuracy. The results suggest that every person produces a unique trajectory of underfoot pressures and that CNNs can learn the distinctive features of these trajectories. Using transfer learning, a few strides could be sufficient to learn and identify new gaits.
Tasks	Gait Recognition, Transfer Learning
Published	2019-07-24
URL	https://arxiv.org/abs/1908.04758v3
PDF	https://arxiv.org/pdf/1908.04758v3.pdf
PWC	https://paperswithcode.com/paper/gait-recognition-via-deep-learning-of-the
Repo
Framework

Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers


Title	Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers
Authors	Aaron Griffith, Andrew Pomerance, Daniel J. Gauthier
Abstract	We explore the hyperparameter space of reservoir computers used for forecasting of the chaotic Lorenz ‘63 attractor with Bayesian optimization. We use a new measure of reservoir performance, designed to emphasize learning the global climate of the forecasted system rather than short-term prediction. We find that optimizing over this measure more quickly excludes reservoirs that fail to reproduce the climate. The results of optimization are surprising: the optimized parameters often specify a reservoir network with very low connectivity. Inspired by this observation, we explore reservoir designs with even simpler structure, and find well-performing reservoirs that have zero spectral radius and no recurrence. These simple reservoirs provide counterexamples to widely used heuristics in the field, and may be useful for hardware implementations of reservoir computers.
Tasks
Published	2019-10-01
URL	https://arxiv.org/abs/1910.00659v2
PDF	https://arxiv.org/pdf/1910.00659v2.pdf
PWC	https://paperswithcode.com/paper/forecasting-chaotic-systems-with-very-low
Repo
Framework

A Unified Deep Learning Approach for Prediction of Parkinson’s Disease


Title	A Unified Deep Learning Approach for Prediction of Parkinson’s Disease
Authors	James Wingate, Ilianna Kollia, Luc Bidaut, Stefanos Kollias
Abstract	The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson’s disease through medical imaging. The approach includes analysis and use of the knowledge extracted by Deep Convolutional and Recurrent Neural Networks (DNNs) when trained with medical images, such as Magnetic Resonance Images and DaTscans. Internal representations of the trained DNNs constitute the extracted knowledge which is used in a transfer learning and domain adaptation manner, so as to create a unified framework for prediction of Parkinson’s across different medical environments. A large experimental study is presented illustrating the ability of the proposed approach to effectively predict Parkinson’s, using different medical image sets from real environments.
Tasks	Domain Adaptation, Transfer Learning
Published	2019-11-25
URL	https://arxiv.org/abs/1911.10653v1
PDF	https://arxiv.org/pdf/1911.10653v1.pdf
PWC	https://paperswithcode.com/paper/a-unified-deep-learning-approach-for
Repo
Framework

A sparse resultant based method for efficient minimal solvers


Title	A sparse resultant based method for efficient minimal solvers
Authors	Snehal Bhayani, Zuzana Kukelova, Janne Heikkilä
Abstract	Many computer vision applications require robust and efficient estimation of camera geometry. The robust estimation is usually based on solving camera geometry problems from a minimal number of input data measurements, i.e. solving minimal problems in a RANSAC framework. Minimal problems often result in complex systems of polynomial equations. Many state-of-the-art efficient polynomial solvers to these problems are based on Gr"obner bases and the action-matrix method that has been automatized and highly optimized in recent years. In this paper we study an alternative algebraic method for solving systems of polynomial equations, i.e., the sparse resultant-based method and propose a novel approach to convert the resultant constraint to an eigenvalue problem. This technique can significantly improve the efficiency and stability of existing resultant-based solvers. We applied our new resultant-based method to a large variety of computer vision problems and show that for most of the considered problems, the new method leads to solvers that are the same size as the the best available Gr"obner basis solvers and of similar accuracy. For some problems the new sparse-resultant based method leads to even smaller and more stable solvers than the state-of-the-art Gr"obner basis solvers. Our new method can be fully automatized and incorporated into existing tools for automatic generation of efficient polynomial solvers and as such it represents a competitive alternative to popular Gr"obner basis methods for minimal problems in computer vision.
Tasks
Published	2019-12-21
URL	https://arxiv.org/abs/1912.10268v1
PDF	https://arxiv.org/pdf/1912.10268v1.pdf
PWC	https://paperswithcode.com/paper/a-sparse-resultant-based-method-for-efficient
Repo
Framework

Deep Neural Network Fingerprinting by Conferrable Adversarial Examples


Title	Deep Neural Network Fingerprinting by Conferrable Adversarial Examples
Authors	Nils Lukas, Yuxuan Zhang, Florian Kerschbaum
Abstract	In Machine Learning as a Service, a provider trains a deep neural network and provides many users access to it. However, the hosted (source) model is susceptible to model stealing attacks where an adversary derives a surrogate model from API access to the source model. For post hoc detection of such attacks, the provider needs a robust method to determine whether a suspect model is a surrogate of their model or not. We propose a fingerprinting method for deep neural networks that extracts a set of inputs from the source model so that only surrogates agree with the source model on the classification of such inputs. These inputs are a specifically crafted subclass of targeted transferable adversarial examples which we call conferrable adversarial examples that transfer exclusively from a source model to its surrogates. We propose new methods to generate these conferrable adversarial examples and use them as our fingerprint. Our fingerprint is the first to be successfully tested as robust against distillation attacks, and our experiments show that this robustness extends to robustness against weaker removal attacks such as fine-tuning, ensemble attacks, adversarial training and stronger adaptive attacks specifically designed against our fingerprint. We even protect against a powerful adversary with white-box access to the source model, whereas the defender only needs black-box access to the surrogate model. We conduct our experiments on the CINIC dataset, which is a superset of CIFAR-10, and a subset of ImageNet32 with 100 classes. Our experiments show that our fingerprint perfectly separates surrogate and reference models. We measure a fingerprint retention of 100% in all evaluated attacks for surrogate models that have at most a difference in test accuracy of five percentage points to the source model.
Tasks
Published	2019-12-02
URL	https://arxiv.org/abs/1912.00888v2
PDF	https://arxiv.org/pdf/1912.00888v2.pdf
PWC	https://paperswithcode.com/paper/deep-neural-network-fingerprinting-by
Repo
Framework

Conditioning a Recurrent Neural Network to synthesize musical instrument transients


Title	Conditioning a Recurrent Neural Network to synthesize musical instrument transients
Authors	Lonce Wyse, Muhammad Huzaifah
Abstract	A recurrent Neural Network (RNN) is trained to predict sound samples based on audio input augmented by control parameter information for pitch, volume, and instrument identification. During the generative phase following training, audio input is taken from the output of the previous time step, and the parameters are externally controlled allowing the network to be played as a musical instrument. Building on an architecture developed in previous work, we focus on the learning and synthesis of transients - the temporal response of the network during the short time (tens of milliseconds) following the onset and offset of a control signal. We find that the network learns the particular transient characteristics of two different synthetic instruments, and furthermore shows some ability to interpolate between the characteristics of the instruments used in training in response to novel parameter settings. We also study the behaviour of the units in hidden layers of the RNN using various visualisation techniques and find a variety of volume-specific response characteristics.
Tasks
Published	2019-03-26
URL	http://arxiv.org/abs/1903.10703v1
PDF	http://arxiv.org/pdf/1903.10703v1.pdf
PWC	https://paperswithcode.com/paper/conditioning-a-recurrent-neural-network-to
Repo
Framework

Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling


Title	Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling
Authors	Yuta Hiasa, Yoshito Otake, Masaki Takao, Takeshi Ogawa, Nobuhiko Sugano, Yoshinobu Sato
Abstract	We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to the segmentation label. We evaluated the performance of the proposed method using two data sets: 20 fully annotated CTs of the hip and thigh regions and 18 partially annotated CTs that are publicly available from The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice coefficient (DC) of 0.891 +/- 0.016 (mean +/- std) and an average symmetric surface distance (ASD) of 0.994 +/- 0.230 mm over 19 muscles in the set of 20 CTs. These results were statistically significant improvements compared to the state-of-the-art hierarchical multi-atlas method which resulted in 0.845 +/- 0.031 DC and 1.556 +/- 0.444 mm ASD. We evaluated validity of the uncertainty metric in the multi-class organ segmentation problem and demonstrated a correlation between the pixels with high uncertainty and the segmentation failure. One application of the uncertainty metric in active-learning is demonstrated, and the proposed query pixel selection method considerably reduced the manual annotation cost for expanding the training data set. The proposed method allows an accurate patient-specific analysis of individual muscle shapes in a clinical routine. This would open up various applications including personalization of biomechanical simulation and quantitative evaluation of muscle atrophy.
Tasks	Active Learning
Published	2019-07-21
URL	https://arxiv.org/abs/1907.08915v2
PDF	https://arxiv.org/pdf/1907.08915v2.pdf
PWC	https://paperswithcode.com/paper/automated-muscle-segmentation-from-clinical
Repo
Framework

ContamiNet: Detecting Contamination in Municipal Solid Waste


Title	ContamiNet: Detecting Contamination in Municipal Solid Waste
Authors	Khoury Ibrahim, Danielle A. Savage, Addie Schnirel, Paul Intrevado, Yannet Interian
Abstract	Leveraging over 30,000 images each with up to 89 labels collected by Recology—an integrated resource recovery company with both residential and commercial trash, recycling and composting services—the authors develop ContamiNet, a convolutional neural network, to identify contaminating material in residential recycling and compost bins. When training the model on a subset of labels that meet a minimum frequency threshold, ContamiNet preforms almost as well human experts in detecting contamination (0.86 versus 0.88 AUC). Recology is actively piloting ContamiNet in their daily municipal solid waste (MSW) collection to identify contaminants in recycling and compost bins to subsequently inform and educate customers about best sorting practices.
Tasks
Published	2019-11-11
URL	https://arxiv.org/abs/1911.04583v1
PDF	https://arxiv.org/pdf/1911.04583v1.pdf
PWC	https://paperswithcode.com/paper/contaminet-detecting-contamination-in
Repo
Framework

Using Gaussian process regression for efficient parameter reconstruction


Title	Using Gaussian process regression for efficient parameter reconstruction
Authors	Philipp-Immanuel Schneider, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger
Abstract	Optical scatterometry is a method to measure the size and shape of periodic micro- or nanostructures on surfaces. For this purpose the geometry parameters of the structures are obtained by reproducing experimental measurement results through numerical simulations. We compare the performance of Bayesian optimization to different local minimization algorithms for this numerical optimization problem. Bayesian optimization uses Gaussian-process regression to find promising parameter values. We examine how pre-computed simulation results can be used to train the Gaussian process and to accelerate the optimization.
Tasks
Published	2019-03-28
URL	http://arxiv.org/abs/1903.12128v1
PDF	http://arxiv.org/pdf/1903.12128v1.pdf
PWC	https://paperswithcode.com/paper/using-gaussian-process-regression-for
Repo
Framework

Sampling Unknown Decision Functions to Build Classifier Copies


Title	Sampling Unknown Decision Functions to Build Classifier Copies
Authors	Irene Unceta, Diego Palacios, Jordi Nin, Oriol Pujol
Abstract	Copies have been proposed as a viable alternative to endow machine learning models with properties and features that adapt them to changing needs. A fundamental step of the copying process is generating an unlabelled set of points to explore the decision behavior of the targeted classifier throughout the input space. In this article we propose two sampling strategies to produce such sets. We validate them in six well-known problems and compare them with two standard methods. We evaluate our proposals in terms of both their accuracy performance and their computational cost.
Tasks
Published	2019-10-01
URL	https://arxiv.org/abs/1910.00237v1
PDF	https://arxiv.org/pdf/1910.00237v1.pdf
PWC	https://paperswithcode.com/paper/sampling-unknown-decision-functions-to-build
Repo
Framework

Personalized Query Auto-Completion Through a Lightweight Representation of the User Context


Title	Personalized Query Auto-Completion Through a Lightweight Representation of the User Context
Authors	Manojkumar Rangasamy Kannadasan, Grigor Aslanyan
Abstract	Query Auto-Completion (QAC) is a widely used feature in many domains, including web and eCommerce search, suggesting full queries based on a prefix typed by the user. QAC has been extensively studied in the literature in the recent years, and it has been consistently shown that adding personalization features can significantly improve the performance of QAC. In this work we propose a novel method for personalized QAC that uses lightweight embeddings learnt through fastText. We construct an embedding for the user context queries, which are the last few queries issued by the user. We also use the same model to get the embedding for the candidate queries to be ranked. We introduce ranking features that compute the distance between the candidate queries and the context queries in the embedding space. These features are then combined with other commonly used QAC ranking features to learn a ranking model. We apply our method to a large eCommerce search engine (eBay) and show that the ranker with our proposed feature significantly outperforms the baselines on all of the offline metrics measured, which includes Mean Reciprocal Rank (MRR), Success Rate (SR), Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (NDCG). Our baselines include the Most Popular Completion (MPC) model as well as a ranking model without our proposed features. The ranking model with the proposed features results in a $20-30%$ improvement over the MPC model on all metrics. We obtain up to a $5%$ improvement over the baseline ranking model for all the sessions, which goes up to about $10%$ when we restrict to sessions that contain the user context. Moreover, our proposed features also significantly outperform text based personalization features studied in the literature before, and adding text based features on top of our proposed embedding based features results only in minor improvements.
Tasks
Published	2019-05-03
URL	https://arxiv.org/abs/1905.01386v1
PDF	https://arxiv.org/pdf/1905.01386v1.pdf
PWC	https://paperswithcode.com/paper/personalized-query-auto-completion-through-a
Repo
Framework

Learning with Inadequate and Incorrect Supervision


Title	Learning with Inadequate and Incorrect Supervision
Authors	Chen Gong, Hengmin Zhang, Jian Yang, Dacheng Tao
Abstract	Practically, we are often in the dilemma that the labeled data at hand are inadequate to train a reliable classifier, and more seriously, some of these labeled data may be mistakenly labeled due to the various human factors. Therefore, this paper proposes a novel semi-supervised learning paradigm that can handle both label insufficiency and label inaccuracy. To address label insufficiency, we use a graph to bridge the data points so that the label information can be propagated from the scarce labeled examples to unlabeled examples along the graph edges. To address label inaccuracy, Graph Trend Filtering (GTF) and Smooth Eigenbase Pursuit (SEP) are adopted to filter out the initial noisy labels. GTF penalizes the l_0 norm of label difference between connected examples in the graph and exhibits better local adaptivity than the traditional l_2 norm-based Laplacian smoother. SEP reconstructs the correct labels by emphasizing the leading eigenvectors of Laplacian matrix associated with small eigenvalues, as these eigenvectors reflect real label smoothness and carry rich class separation cues. We term our algorithm as `Semi-supervised learning under Inadequate and Incorrect Supervision’ (SIIS). Thorough experimental results on image classification, text categorization, and speech recognition demonstrate that our SIIS is effective in label error correction, leading to superior performance to the state-of-the-art methods in the presence of label noise and label scarcity. \|
Tasks	Image Classification, Speech Recognition, Text Categorization
Published	2019-02-20
URL	http://arxiv.org/abs/1902.07429v1
PDF	http://arxiv.org/pdf/1902.07429v1.pdf
PWC	https://paperswithcode.com/paper/learning-with-inadequate-and-incorrect
Repo
Framework

Segmenting Potentially Cancerous Areas in Prostate Biopsies using Semi-Automatically Annotated Data


Title	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
Abstract	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.
Tasks
Published	2019-04-15
URL	http://arxiv.org/abs/1904.06969v1
PDF	http://arxiv.org/pdf/1904.06969v1.pdf
PWC	https://paperswithcode.com/paper/segmenting-potentially-cancerous-areas-in
Repo
Framework

Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations


Title	Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations
Authors	Liam Hiley, Alun Preece, Yulia Hicks
Abstract	The popularity of Deep Learning for real-world applications is ever-growing. With the introduction of high performance hardware, applications are no longer limited to image recognition. With the introduction of more complex problems comes more and more complex solutions, and the increasing need for explainable AI. Deep Neural Networks for Video tasks are amongst the most complex models, with at least twice the parameters of their Image counterparts. However, explanations for these models are often ill-adapted to the video domain. The current work in explainability for video models is still overshadowed by Image techniques, while Video Deep Learning itself is quickly gaining on methods for still images. This paper seeks to highlight the need for explainability methods designed with video deep learning models, and by association spatio-temporal input in mind, by first illustrating the cutting edge for video deep learning, and then noting the scarcity of research into explanations for these methods.
Tasks	Video Recognition
Published	2019-09-07
URL	https://arxiv.org/abs/1909.05667v1
PDF	https://arxiv.org/pdf/1909.05667v1.pdf
PWC	https://paperswithcode.com/paper/explainable-deep-learning-for-video
Repo
Framework

Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning


Title	Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning
Authors	Takanori Fujiwara, Oh-Hyun Kwon, Kwan-Liu Ma
Abstract	Dimensionality reduction (DR) is frequently used for analyzing and visualizing high-dimensional data as it provides a good first glance of the data. However, to interpret the DR result for gaining useful insights from the data, it would take additional analysis effort such as identifying clusters and understanding their characteristics. While there are many automatic methods (e.g., density-based clustering methods) to identify clusters, effective methods for understanding a cluster’s characteristics are still lacking. A cluster can be mostly characterized by its distribution of feature values. Reviewing the original feature values is not a straightforward task when the number of features is large. To address this challenge, we present a visual analytics method that effectively highlights the essential features of a cluster in a DR result. To extract the essential features, we introduce an enhanced usage of contrastive principal component analysis (cPCA). Our method, called ccPCA (contrasting clusters in PCA), can calculate each feature’s relative contribution to the contrast between one cluster and other clusters. With ccPCA, we have created an interactive system including a scalable visualization of clusters’ feature contributions. We demonstrate the effectiveness of our method and system with case studies using several publicly available datasets.
Tasks	Dimensionality Reduction
Published	2019-05-10
URL	https://arxiv.org/abs/1905.03911v3
PDF	https://arxiv.org/pdf/1905.03911v3.pdf
PWC	https://paperswithcode.com/paper/supporting-analysis-of-dimensionality
Repo
Framework