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an lstm autoencoder could be used for predicting the next step in the sequence.
this would render clustering redundant, as given predicted features, a single closest matching frame could be found using a k-d tree, similarly to audio mosaicking. https://machinelearningmastery.com/lstm-autoencoders/
would it solve the general messiness of the model output experienced currently?
worth trying
The text was updated successfully, but these errors were encountered:
an lstm autoencoder could be used for predicting the next step in the sequence.
this would render clustering redundant, as given predicted features, a single closest matching frame could be found using a k-d tree, similarly to audio mosaicking.
https://machinelearningmastery.com/lstm-autoencoders/
would it solve the general messiness of the model output experienced currently?
worth trying
The text was updated successfully, but these errors were encountered: