FREQUENCY-AWARE MASKED AUTOENCODERS FOR MULTIMODAL PRETRAINING ON FREQUENCY-BASED SIGNALS
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United States of America
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Jan 30, 2025
app pub date -
Apr 16, 2024
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Apr 16, 2024
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Abstract
The subject technology provides frequency-aware masked autoencoders for multimodal pretraining on frequency-based signals. An apparatus receives input data comprising frequency-based signal information associated with one or more modalities. The apparatus transforms the input data from a time domain to a frequency domain. The apparatus generates a frequency-embedded latent representation of the input data comprising time-domain and frequency-domain information. The apparatus also generates a masked frequency-embedded latent representation by masking one or more frequency components in the frequency-embedded latent representation. The apparatus produces a trained machine learning model by training a neural network to predict one or more masked frequency components of the frequency-embedded latent representation.
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