1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao...
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Transcript of 1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao...
![Page 1: 1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao Duke University Jason Stack Office of Naval Research.](https://reader036.fdocuments.us/reader036/viewer/2022062523/5a4d1af87f8b9ab059982d1e/html5/thumbnails/1.jpg)
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Multi-Task Semi-Supervised Underwater Mine Detection
Lawrence Carin, Qiuhua Liu and Xuejun LiaoDuke University
Jason StackOffice of Naval Research
![Page 2: 1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao Duke University Jason Stack Office of Naval Research.](https://reader036.fdocuments.us/reader036/viewer/2022062523/5a4d1af87f8b9ab059982d1e/html5/thumbnails/2.jpg)
Intra-Scene Context
![Page 3: 1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao Duke University Jason Stack Office of Naval Research.](https://reader036.fdocuments.us/reader036/viewer/2022062523/5a4d1af87f8b9ab059982d1e/html5/thumbnails/3.jpg)
What Analyst Processes Individual Signatures Processedby Supervised Classifiers
Message:
Analyst Places Classification of Any Given Item Within Context of All Items in the SceneSupervised Classifier Classifies Each Item in Isolation
![Page 4: 1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao Duke University Jason Stack Office of Naval Research.](https://reader036.fdocuments.us/reader036/viewer/2022062523/5a4d1af87f8b9ab059982d1e/html5/thumbnails/4.jpg)
Decision surface based on labeled data (supervised)
Decision surface based on labeled & Unlabeled data (semi-supervised)
![Page 5: 1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao Duke University Jason Stack Office of Naval Research.](https://reader036.fdocuments.us/reader036/viewer/2022062523/5a4d1af87f8b9ab059982d1e/html5/thumbnails/5.jpg)
Inter-Scene Context
![Page 6: 1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao Duke University Jason Stack Office of Naval Research.](https://reader036.fdocuments.us/reader036/viewer/2022062523/5a4d1af87f8b9ab059982d1e/html5/thumbnails/6.jpg)
![Page 7: 1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao Duke University Jason Stack Office of Naval Research.](https://reader036.fdocuments.us/reader036/viewer/2022062523/5a4d1af87f8b9ab059982d1e/html5/thumbnails/7.jpg)
![Page 8: 1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao Duke University Jason Stack Office of Naval Research.](https://reader036.fdocuments.us/reader036/viewer/2022062523/5a4d1af87f8b9ab059982d1e/html5/thumbnails/8.jpg)
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Message
Humans are very good at exploiting context, both within a given scene and across multiple scenes
Intra-scene context: semi-supervised learning
Inter-scene context: multi-task and transfer learning
A major focus of machine learning these days
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Data Manifold Representation Based on Markov Random Walks
Given X={x1, …,xN}, first construct a graph G=(X,W), with the affinity matrix W, where the (i, j)-th element of W is defined by a Gaussian kernel:
we consider a Markov transition matrix A, which defines a Markov random walk, where the (i, j)-th element:
gives the probability of walking from xi to xj by a single step.
The one-step Markov random work provides a local similarity measure between data points.
)2/exp( 22
ijiij xxw
N
k ik
ijij
w
wa
1
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Semi-Supervised Multitask Learning(1/2)
Semi-supervised MTL: Given M partially labeled data manifolds, each defining a classification task, we propose a unified sharing structure to learn the M classifiers simultaneously.
The Sharing Prior: We consider M PNBC classifiers, parameterized by
The M classifiers are not independent
but coupled by a joint prior distribution:
,m....,2,1 Mm
M
mmmM pp
1111 ),..,|(),..,(
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Semi-Supervised Multitask Learning(2/2)
With
The normal distributions indicates the meta-knowledge indicating how the present task should be learned, based on the experience with a previous task.
When there are no previous tasks, only the baseline prior is used by setting m=1 =>PNBC.
Sharing tasks to have similar , not exactly the same(advantages over the Dirac delta function used in previous MTL work).
s'
1
1
211 ),;()|(
11),..,|(
m
lmllmmmm Np
mp Iγ
Baseline prior Prior transferred from previous tasks
Balance parameter
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![Page 16: 1 Multi-Task Semi-Supervised Underwater Mine Detection Lawrence Carin, Qiuhua Liu and Xuejun Liao Duke University Jason Stack Office of Naval Research.](https://reader036.fdocuments.us/reader036/viewer/2022062523/5a4d1af87f8b9ab059982d1e/html5/thumbnails/16.jpg)
Thanks