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Zero-shot learning for holographic context analysis in microplastics probing
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Researchers developed a zero-shot machine learning method using attribute embedding for holographic image analysis to identify unknown microplastic types without requiring labelled training data for every class. The approach reduces the need for manual dataset annotation and improves practical deployment of microplastic detection systems.
A zero-shot learning method with attribute embedding is developed for holographic image analysis and microplastics probing. Experimental results show its efficacy in identifying the unknown microplastics and alleviating the need for manual dataset class annotation.
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Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.