A survey of on-device learning in TinyML organized by distribution change regimes, highlighting influences on applications, hardware, and solutions plus a gap between benchmarks and deployments.
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GeMCL achieves stable 1000-class few-shot spoken word classification with 5 shots per class, comparable to finetuned HuBERT but 2000x faster adaptation using less data and time.
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What changes after deployment? A survey on On-device Learning in TinyML
A survey of on-device learning in TinyML organized by distribution change regimes, highlighting influences on applications, hardware, and solutions plus a gap between benchmarks and deployments.
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Scaling few-shot spoken word classification with generative meta-continual learning
GeMCL achieves stable 1000-class few-shot spoken word classification with 5 shots per class, comparable to finetuned HuBERT but 2000x faster adaptation using less data and time.