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Rare Event Detection using Disentangled Representation Learning
[article]
2018
arXiv
pre-print
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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
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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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]
2020
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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
2021
Scientific Reports
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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]
2021
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2021
Security and Communication Networks
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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
2020
Applied Sciences
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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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