ArticleFrontiers in neurorobotics2024
Brain-inspired semantic data augmentation for multi-style images.
Article in Frontiers in neurorobotics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- TourismNeuro xLSTM: neuro-inspired xLSTM for rural tourism planning and innovation.Frontiers in computational neuroscience · 2025Article
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3 authors.
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Abstract
Data augmentation is an effective technique for automatically expanding training data in deep learning. Brain-inspired methods are approaches that draw inspiration from the functionality and structure of the human brain and apply these mechanisms and principles to artificial intelligence and computer science. When there is a large style difference between training data and testing data, common data augmentation methods cannot effectively enhance the generalization performance of the deep model. To solve this problem, we improve modeling Domain Shifts with Uncertainty (DSU) and propose a new brain-inspired computer vision image data augmentation method which consists of two key components, namely,
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