VISAL-A novel learning strategy to address class imbalance.
Neural Netw
; 161: 178-184, 2023 Apr.
Article
in English
| MEDLINE | ID: covidwho-2236547
ABSTRACT
In the imbalance data scenarios, Deep Neural Networks (DNNs) fail to generalize well on minority classes. In this letter, we propose a simple and effective learning function i.e, Visually Interpretable Space Adjustment Learning (VISAL) to handle the imbalanced data classification task. VISAL's objective is to create more room for the generalization of minority class samples by bringing in both the angular and euclidean margins into the cross-entropy learning strategy. When evaluated on the imbalanced versions of CIFAR, Tiny ImageNet, COVIDx and IMDB reviews datasets, our proposed method outperforms the state of the art works by a significant margin.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Algorithms
/
Neural Networks, Computer
Type of study:
Experimental Studies
/
Randomized controlled trials
Language:
English
Journal:
Neural Netw
Journal subject:
Neurology
Year:
2023
Document Type:
Article
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