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Detection and Localisation of Multiple In-Core Perturbations with Neutron Noise-Based Self-Supervised Domain Adaptation
2021
Zenodo
Preserved Fulltext
. • Derivation of core perturbation characteristics to classify and locate its origin. • Yet this is challenging due to the limited number of neutron detectors in western type reactors. • We ask, can we ...
target input sample and processed simultaneously by the network. ...
Kollias. "3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection. " EPJ Nuclear Sciences & Technologies (2019). ...
doi:10.5281/zenodo.5575851
fatcat:nd45vlpcsnawzopfyn3hz44zji
Joint Symmetry Detection and Shape Matching for Non-Rigid Point Cloud
[article]
2022
arXiv
pre-print
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Despite the success of deep functional maps in non-rigid 3D shape matching, there exists no learning framework that models both self-symmetry and shape matching simultaneously. ...
In this paper, we propose a novel framework that simultaneously learns both self symmetry as well as a pairwise map between a pair of shapes. ...
Our work also follows a similar direction as we aim to learn shape matching and symmetry detection simultaneously. ...
arXiv:2112.02713v2
fatcat:riu4uphounh5fldmqfdrawowgq
Detection and localisation of multiple in-core perturbations with neutron noise-based self-supervised domain adaptation
2021
Zenodo
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. • Derivation of core perturbation characteristics to classify and locate its origin. • Yet this is challenging due to the limited number of neutron detectors in western type reactors. • We ask, can we ...
Data Acquisition • Real plant measurements are difficult to obtain, unlabelled, and anomalies are thankfully rare. • As such it is beneficial to have alternative means to train our algorithms. • Supervised ...
• Importantly how can we make an arbitrary number of predictions per sample that change between samples? ...
doi:10.5281/zenodo.4438588
fatcat:deua6jo5dfccfkqbmrrid6fhj4
Who's that Actor? Automatic Labelling of Actors in TV series starting from IMDB Images
[article]
2016
arXiv
pre-print
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To bridge this gap, we propose a graph-matching based self-labelling algorithm, which we coin HSL (Hungarian Self Labeling). ...
In each series as well, there is considerable change in actor appearance due to makeup, lighting, ageing, etc. ...
Active speakers detected by lip movement detection in video can then be labelled from the aligned subtitle and transcript files. ...
arXiv:1611.09162v1
fatcat:y2dhbx3rtzgtlev7nivio6vyji
Region Similarity Representation Learning
[article]
2021
arXiv
pre-print
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We present Region Similarity Representation Learning (ReSim), a new approach to self-supervised representation learning for localization-based tasks such as object detection and segmentation. ...
Through object detection, instance segmentation, and dense pose estimation experiments, we illustrate how ReSim learns representations which significantly improve the localization and classification performance ...
[6] where the authors observed that "linear classification accuracy is not monotonically related to transfer performance in detection." ...
arXiv:2103.12902v2
fatcat:x3j4gwqdbrbormhpw557t33ytu
Self-supervised Moving Vehicle Tracking with Stereo Sound
[article]
2019
arXiv
pre-print
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as a form of self-supervision, without resorting to the collection of ground-truth annotations. ...
During training, knowledge embodied in a well-established visual vehicle detection model is transferred to the audio domain using unlabeled videos as a bridge. ...
Cross-modal Self-supervised Learning Our work is in the domain of self-supervised learning, which exploits implicit labels that are freely available in the structure of the data. ...
arXiv:1910.11760v1
fatcat:4mkpm2bo75aerb6w5uampuzrrm
Generative and self-supervised domain adaptation for one-stage object detection
2021
Array
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Self-supervision-based methods [17, 18, 19 ] employ self-supervised tasks, such as reconstruction, image rotation prediction, and self-training. ...
To take advantage of both the adversarial generative method and self-supervision-based method, we introduced a generative and self-supervised domain adaptation method. ...
doi:10.1016/j.array.2021.100071
fatcat:ovrgangnk5atjb7gms44walbka
Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training
[article]
2022
arXiv
pre-print
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STMono3D achieves remarkable performance on all evaluated datasets and even surpasses fully supervised results on the KITTI 3D object detection dataset. ...
To mitigate the depth-shift, we introduce the geometry-aligned multi-scale training strategy to disentangle the camera parameters and guarantee the geometry consistency of domains. ...
Before passing to the models, both the target and source domain input are further augmented by the GAMS strategy in Sec. 3.4, where images and camera intrinsic parameters are cautiously aligned via simultaneously ...
arXiv:2204.11590v2
fatcat:oho5oi4go5fxtavavfkqeoiqme
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
[article]
2018
arXiv
pre-print
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2018 pre-print arXiv:1804.06353v1 fatcat:pba3loq6jbdxjhieyapmxpw4fq
We review semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis/detection or segmentation tasks. ...
We also discuss connections between these learning scenarios, and opportunities for future research. ...
simultaneously. ...
arXiv:1804.06353v2
fatcat:xke66fsrgjanjmopwti5tze4fm
Self-Guided Adaptation: Progressive Representation Alignment for Domain Adaptive Object Detection
[article]
2020
arXiv
pre-print
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In this work, we propose a Self-Guided Adaptation (SGA) model, target at aligning feature representation and transferring object detection models across domains while considering the instantaneous alignment ...
Using multi-stage convolutional features, SGA is further aggregated to fully align hierarchical representations of detection models. ...
In this study, we propose a Self-Guided Adaptation (SGA) model with a Self-Guided Progressive Sampling(SPS) strategy, and target at aligning feature representation and transferring detection models across ...
arXiv:2003.08777v2
fatcat:6xj52frxqrerrnx2lmtzh7cm2i
Track-based self-supervised classification of dynamic obstacles
2010
Autonomous Robots
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This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. ...
Experiments show that the system is able to achieve 95% classification accuracy and to maintain the performance through on-line retraining when working conditions change. ...
This work is supported by the Australian Research Council (ARC) Centre of Excellence program and the New South Wales Government. ...
doi:10.1007/s10514-010-9193-0
fatcat:aw57zqdtg5gsff5ovvxqtwa7ie
Unaligned Supervision For Automatic Music Transcription in The Wild
[article]
2022
arXiv
pre-print
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We introduce NoteEM, a method for simultaneously training a transcriber and aligning the scores to their corresponding performances, in a fully-automated process. ...
The scores are typically aligned using audio features and strenuous human intervention to generate training labels. ...
Since accurate velocity information cannot be derived from separate-source midi, we believe self-supervision is the main direction for training velocity detection, and we leave this to future work. ...
arXiv:2204.13668v1
fatcat:bkqzv3iwb5cqxb6t673mvowamy
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training
[article]
2022
arXiv
pre-print
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Notably, we further design a novel global and local representation learning scheme to implicitly regularize the networks to utilize noisy samples in a self-supervised manner. ...
Specifically, we employ the self-ensemble model with a noisy label filter to efficiently select the clean and noisy samples. ...
Learning curve of global alignment loss. In Fig. 5 (c), we plot the change of global inter-sample relationship with and without the global alignment loss. ...
arXiv:2205.04723v1
fatcat:zmumvcntnzdtfbauvt7nba2cyy
Attention-Based Self-Supervised Feature Learning for Security Data
[article]
2020
arXiv
pre-print
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The learned features are used in an anomaly detection model and perform better than learned features from baseline methods. ...
In this paper, we design a self-supervised sequence-to-sequence model with attention to learn an embedding for data routinely used in cyber-security applications. ...
Conclusions We explored multiple methods for unsupervised and self-supervised feature learning for security data. ...
arXiv:2003.10639v1
fatcat:lqkqpfqj6bbknpxgwci7bifrdu
COMPUTATIONAL ANALYSIS ON GENE EXPRESSION PATTERN: A SURVEY
2018
International Journal of Advanced Research
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Sequence
analysis and
alignment
11 ...
As part of the chromosome, the sub-functions are self-learned or self-evolved. ...
doi:10.21474/ijar01/7270
fatcat:n73sxl6hurernpjbdp54aau4pq
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