Machine learning methods including denoising autoencoders, unsupervised interference mitigation, blind source separation, and certifiable classification are developed and experimentally validated to improve multi-species laser spectroscopy under complex conditions.
Generalizing from a few examples: A survey on few-shot learning,
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Larger pre-training data scale and class diversity improve audio transfer learning performance, yet similarity between pre-training and target task has a stronger positive effect.
citing papers explorer
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Machine Learning Enhanced Laser Spectroscopy for Multi-Species Gas Detection in Complex and Harsh Environments
Machine learning methods including denoising autoencoders, unsupervised interference mitigation, blind source separation, and certifiable classification are developed and experimentally validated to improve multi-species laser spectroscopy under complex conditions.
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How Class Ontology and Data Scale Affect Audio Transfer Learning
Larger pre-training data scale and class diversity improve audio transfer learning performance, yet similarity between pre-training and target task has a stronger positive effect.