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Rare Event Detection using Disentangled Representation Learning [article]

Ryuhei Hamaguchi, Ken Sakurada, Ryosuke Nakamura
2018 arXiv   pre-print
that disentangle rare events from trivial ones.  ...  In order to overcome these difficulties, we propose a novel method to learn disentangled representations from only low-cost negative samples.  ...  The models are trained using only negative (i.e. normal) data, and rare events are detected as outliers. Model architecture for representation learning.  ... 
arXiv:1812.01285v1 fatcat:culwl26pffcvjl4s4c25jk5xdu

Rare Event Detection Using Disentangled Representation Learning

Ryuhei Hamaguchi, Ken Sakurada, Ryosuke Nakamura
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
that disentangle rare events from trivial ones.  ...  In order to overcome these difficulties, we propose a novel method to learn disentangled representations from only low-cost negative samples.  ...  The models are trained using only negative (i.e. normal) data, and rare events are detected as outliers. Model architecture for representation learning.  ... 
doi:10.1109/cvpr.2019.00955 dblp:conf/cvpr/HamaguchiSN19 fatcat:5dopbpkugbbw3llx7xfddgg4yq

Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection [article]

Liwei Lin, Xiangdong Wang, Hong Liu, Yueliang Qian
2020 arXiv   pre-print
In this paper, a special decision surface for the weakly-supervised sound event detection (SED) and a disentangled feature (DF) for the multi-label problem in polyphonic SED are proposed.  ...  As for the problem of the unbalanced dataset and the co-occurrence of multiple categories in the polyphonic event detection task, we propose a DF to reduce interference among categories, which optimizes  ...  Index Terms-Sound event detection, machine learning, weakly-supervised learning, attention pooling. I.  ... 
arXiv:1905.10091v6 fatcat:rpm2m4tskjdirnq6vfw2pzqltm

Variational Disentanglement for Rare Event Modeling [article]

Zidi Xiu, Chenyang Tao, Michael Gao, Connor Davis, Benjamin A. Goldstein, Ricardo Henao
2021 arXiv   pre-print
So motivated, we propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems.  ...  Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems  ...  To allow for more efficient learning from the rare events, we make some further assumptions to regularize the latent representation: (i) effect disentanglement: the contribution from each dimension of  ... 
arXiv:2009.08541v5 fatcat:ft5gmy423bgxronz4kokr5w66i

Spatio-Temporal Action Graph Networks [article]

Roei Herzig, Elad Levi, Huijuan Xu, Hang Gao, Eli Brosh, Xiaolong Wang, Amir Globerson, Trevor Darrell
2019 arXiv   pre-print
We propose a novel inter-object graph representation for activity recognition based on a disentangled graph embedding with direct observation of edge appearance.  ...  We employ a novel factored embedding of the graph structure, disentangling a representation hierarchy formed over spatial dimensions from that found over temporal variation.  ...  Overall, the vehicles collected more than 10 million rides, and rare collision events were automatically detected using a triggering algorithm based on the IMU and gyroscope sensor data. 3 The events  ... 
arXiv:1812.01233v2 fatcat:hcx6yyganrcedl5gyvwss3xb5u

Pattern discovery and disentanglement on relational datasets

Andrew K. C. Wong, Pei-Yuan Zhou, Zahid A. Butt
2021 Scientific Reports  
AbstractMachine Learning has made impressive advances in many applications akin to human cognition for discernment.  ...  Hence, PDD promises broad and ground-breaking applications in genomic and biomedical machine learning.  ...  . www.nature.com/scientificreports/ Besides the inserted rare group, PDD can also detect the rare cases.  ... 
doi:10.1038/s41598-021-84869-4 pmid:33707478 pmcid:PMC7952710 fatcat:vulfhp3bvvhcbgsgwlxguk7rvu

Cohort Characteristics and Factors Associated with Cannabis Use among Adolescents in Canada Using Pattern Discovery and Disentanglement Method [article]

Peiyuan Zhou, Andrew K.C. Wong, Yang Yang, Scott T. Leatherdale, Kate Battista, Zahid A. Butt, George Michalopoulos, Helen Chen
2021 arXiv   pre-print
We then used the Pattern Discovery and Disentanglement (PDD) algorithm that we have developed to detect strong and rare (yet statistically significant) associations from the dataset.  ...  We aimed to discover significant frequent/rare associations of behavioral factors among Canadian adolescents related to cannabis use.  ...  Table 4 . 4 Detected Rare Associations for Cannabis Use  ... 
arXiv:2109.01739v1 fatcat:rddr2n5onfdn5df4p5h2fbthqy

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks [article]

Marco Schreyer, Timur Sattarov, Christian Schulze, Bernd Reimer, and Damian Borth
2019 arXiv   pre-print
The learned representation provides a holistic view on a given set of journal entries and significantly improves the interpretability of detected accounting anomalies.  ...  We demonstrate that such artificial neural networks are capable of learning a semantic meaningful representation of real-world journal entries.  ...  However, such tests often result in a high volume of false-positive alerts due to rare but regular events such as reverse postings, provisions and year-end adjustments usually associated with a low fraud  ... 
arXiv:1908.00734v1 fatcat:iew3huhrprfyzmdfiurkobj5zm

Causal Imitative Model for Autonomous Driving [article]

Mohammad Reza Samsami, Mohammadhossein Bahari, Saber Salehkaleybar, Alexandre Alahi
2021 arXiv   pre-print
Imitation learning is a powerful approach for learning autonomous driving policy by leveraging data from expert driver demonstrations.  ...  Specifically, CIM disentangles the input to a set of latent variables, selects the causal variables, and determines the next position by leveraging the selected variables.  ...  Instead of directly learning the policy from input representation (conventional approach), we first detect the causes and then, use them to train the policy (proposed approach).  ... 
arXiv:2112.03908v1 fatcat:vtw5kdygjvd2tp3p6fods4k2em

NestedVAE: Isolating Common Factors via Weak Supervision [article]

Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
2020 arXiv   pre-print
An evaluation of NestedVAE on both domain and attribute invariance, change detection, and learning common factors for the prediction of biological sex demonstrates that NestedVAE significantly outperforms  ...  Two outer VAEs with shared weights attempt to reconstruct the input and infer a latent space, whilst a nested VAE attempts to reconstruct the latent representation of one image, from the latent representation  ...  VAE [6] 0.5724 Adversarial VAE [61] 0.5834 Multi-Level VAE [13] 0.6072 Rare-Event VAE [33] 0.7166 NestedVAE (ours) 0.7380 Table 2 . Change detection accuracy on rotated MNIST.  ... 
arXiv:2002.11576v1 fatcat:nb4emnyn4vbg5adv6n4o53f65e

NestedVAE: Isolating Common Factors via Weak Supervision

Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
An evaluation of NestedVAE on both domain and attribute invariance, change detection, and learning common factors for the prediction of biological sex demonstrates that NestedVAE significantly outperforms  ...  Two outer VAEs with shared weights attempt to reconstruct the input and infer a latent space, whilst a nested VAE attempts to reconstruct the latent representation of one image, from the latent representation  ...  VAE [6] 0.5724 Adversarial VAE [60] 0.5834 Multi-Level VAE [13] 0.6072 Rare-Event VAE [32] 0.7166 NestedVAE (ours) 0.7380 Table 2 . Change detection accuracy on rotated MNIST.  ... 
doi:10.1109/cvpr42600.2020.00922 dblp:conf/cvpr/VowelsCB20 fatcat:ekbdzca4jjdq5bkdcv3lbezlye

Bump Hunting in Latent Space [article]

Blaž Bortolato, Barry M. Dillon, Jernej F. Kamenik, Aleks Smolkovič
2021 arXiv   pre-print
Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC.  ...  To this end, we introduce a physics inspired variational autoencoder (VAE) architecture which performs competitively and robustly on the LHC Olympics Machine Learning Challenge datasets.  ...  In particular, anomaly detection techniques address the problem of searching for rare a-priori unknown signals in isolated regions of measured phase-space.  ... 
arXiv:2103.06595v1 fatcat:tfst6kdtvzcelmnrn5wtuz33ge

Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection [article]

Lei Zhong, Juan Cao, Qiang Sheng, Junbo Guo, Ziang Wang
2020 arXiv   pre-print
As to the third limitation, we extend our model to Disentangled TPC-GCN (DTPC-GCN), to disentangle topic-related and topic-unrelated features and then fuse dynamically.  ...  Identifying controversial posts on social media is a fundamental task for mining public sentiment, assessing the influence of events, and alleviating the polarized views.  ...  EQ3: Can DTPC-GCN learn disentangled features and dynamically fuse them for controversy detection? Dataset We perform our experiments on two real-world datasets in different languages.  ... 
arXiv:2005.07886v1 fatcat:m7hpjisvl5f7nfy6xruihtrpgi

An Autonomous Cyber-Physical Anomaly Detection System Based on Unsupervised Disentangled Representation Learning

Chunyu Li, Xiaobo Guo, Xiaowei Wang, Konstantinos Demertzis
2021 Security and Communication Networks  
In this paper, considering the high importance of the operational status of CPS for heavy industry, an innovative autonomous anomaly detection system based on unsupervised disentangled representation learning  ...  It is a temporal disentangled variational autoencoder (TDVA) which, mimicking the process of rapid human intuition, using high- or low-dimensional reasoning, finds and models the useful information independently  ...  The Proposed Unsupervised Disentangled Representation Learning System is paper presents and evaluates an Autonomous Cyber-Physical Anomaly Detection System that uses an unsupervised disentangled representation  ... 
doi:10.1155/2021/1626025 fatcat:wivtxhoefrhsfdwj7nzu56rxia

Special Issue on Advances in Deep Learning

Diego Gragnaniello, Andrea Bottino, Sandro Cumani, Wonjoon Kim
2020 Applied Sciences  
Nowadays, deep learning is the fastest growing research field in machine learning and has a tremendous impact on a plethora of daily life applications, ranging from security and surveillance to autonomous  ...  This work presents an interesting denoising approach based on the disentangling of the representation of the useful signal and the noise.  ...  The authors of [4] approach the identification of anomalous events for video surveillance tasks by using a 3D CNN that is merely trained on "normal" events.  ... 
doi:10.3390/app10093172 fatcat:kdowatxbprdhbkmox62nlqyquq
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