| CPC G06N 3/08 (2013.01) [G06N 3/0455 (2023.01); G06N 3/0495 (2023.01)] | 20 Claims |
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1. A system for upsampling compressed data using a jointly trained vector quantized variational autoencoder (VQ-VAE) neural upsampler, comprising:
a computing device comprising at least a memory and a processor;
a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to:
compress input data into a discrete latent representation using a VQ-VAE encoder;
store the compressed representation in a discrete latent space;
reconstruct the compressed data from the latent representation using a VQ-VAE decoder;
enhance the reconstructed data using a neural upsampler to recover information lost during compression;
jointly train the VQ-VAE and neural upsampler by iteratively updating their parameters based on a joint loss function that combines the reconstruction loss of the VQ-VAE and the upsampling loss of the neural upsampler; and
explore and manipulate the discrete latent space learned by the VQ-VAE to generate new or modified data using techniques comprising interpolation, extrapolation, and vector arithmetic.
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