Conference Paper/Proceeding/Abstract 201 views 5 downloads
Active Deep Clustering: Exploratory Analysis to Assist in Decision-Making on Incremental Label Morphing Datasets
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Xianghua Xie
ACIVS 2025 / LNCS
Swansea University Authors:
Connor Clarkson, Mike Edwards , Xianghua Xie
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Abstract
Many supervised-based training schemes rely on the need to have a single associated label for each data sample within a set. Where the goal is to learn different levels of granularity of the data in an implicit form, in the context of neural networks, this is accomplished with different layers to ex...
| Published in: | ACIVS 2025 / LNCS |
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| Published: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70372 |
| Abstract: |
Many supervised-based training schemes rely on the need to have a single associated label for each data sample within a set. Where the goal is to learn different levels of granularity of the data in an implicit form, in the context of neural networks, this is accomplished with different layers to extract features at distinct levels. In this work, we explore more explicit labelling structures where each sample has multiple labels forming relationships at both an abstract and fine-grained level, producing a tree for each associated data sample. This novel type of training scheme utilises a refinement strategy based on deep clustering approaches to detect the colliding and splitting of clusters where each is assigned a label. Experts can then be queried to determine if those colliding clusters should belong to a single label, or alternatively, if they are splitting, should we create new labels, forming an active component of our training scheme. Colliding clusters form a parent label while splitting clusters form sibling labels within the tree structure. By utilizing a tree data structure to represent labels at different levels of granularity, we can invoke explicitly defined relationships and dependencies to form a more structured and interpretable representation of data. Instead of treating data as flat, homogenous sets, we allow for the exploitation of hierarchical relationships and leverage inherent structure to improve data efficiency. We present a case study of the approach applied within the steel manufacturing domain, where quality control remains an active challenge due to morphing labels as products move down the production line. |
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| College: |
Faculty of Science and Engineering |