A transformer-based variational autoencoder applies time series augmentations in its latent space, claiming better control and fidelity than direct augmentation, but the reported Wasserstein results contradict that claim on one of three datasets.
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L-GTA: Latent Generative Modeling for Time Series Augmentation
A transformer-based variational autoencoder applies time series augmentations in its latent space, claiming better control and fidelity than direct augmentation, but the reported Wasserstein results contradict that claim on one of three datasets.