An error-based contrastive training loss that pushes sensor features away from near-miss transcripts improves IMU handwriting recognition with zero inference overhead, but gains are split-specific.
In: 2012 16th International Symposium on Wearable Computers
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Enhancing IMU-Based Online Handwriting Recognition via Contrastive Learning with Zero Inference Overhead
An error-based contrastive training loss that pushes sensor features away from near-miss transcripts improves IMU handwriting recognition with zero inference overhead, but gains are split-specific.