Keywords: Domain Adaptation, Latent Space Transfer, American Sign Language
Abstract: The main advantage of wearable devices lies in enabling them to be tracked without external infrastructure. However, unlike vision (cameras), there is a dearth of large-scale training data to develop robust ML models for wearable devices. SIHeDA-Net (Sensor-Image Heterogeneous Domain Adaptation) uses training data from public images of American Sign Language (ASL) that can be used for inferences on sensors even with errors by bridging the domain gaps through latent space transfer.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Transfer Learning and Domain Adaptation
Secondary Subject Area: Learning with Noisy Labels and Limited Data
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TL;DR: Exploring heterogeneous domain transfer for gesture recognition. Our model, SIHeDA-Net, uses images of American Sign Language (ASL) as inferences on noisy sensor data by bridging the domain gaps through latent space transfer.
Code And Data: https://github.com/spider-tronix/SIHeDA-Net