DLGAN, a dual-layer GAN with a supervised sequence autoencoder and feature-space GAN, generates synthetic time series with stronger temporal dependencies than six baselines on four benchmark datasets.
Em- pirical evaluation of gated recurrent neural networks on sequence modeling
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks
DLGAN, a dual-layer GAN with a supervised sequence autoencoder and feature-space GAN, generates synthetic time series with stronger temporal dependencies than six baselines on four benchmark datasets.