# Semi-Recurrent CNN-based VAE-GAN for Sequential Data Generation

arXiv: https://arxiv.org/abs/1806.00509 · HTML: https://mlsanity.com/paper/1806.00509/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition](https://mlsanity.com/paper/2102.03777/) (2021)
- [Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning](https://mlsanity.com/paper/2110.07717/) (2021)
- [GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy](https://mlsanity.com/paper/2205.08886/) (2022)
- [Research and application of artificial intelligence based webshell detection model: A literature review](https://mlsanity.com/paper/2405.00066/) (2024)
- [NSSI-Net: A Multi-Concept GAN for Non-Suicidal Self-Injury Detection Using High-Dimensional EEG in a Semi-Supervised Framework](https://mlsanity.com/paper/2410.12159/) (2024)

## Abstract (arXiv)

A semi-recurrent hybrid VAE-GAN model for generating sequential data is introduced. In order to consider the spatial correlation of the data in each frame of the generated sequence, CNNs are utilized in the encoder, generator, and discriminator. The subsequent frames are sampled from the latent distributions obtained by encoding the previous frames. As a result, the dependencies between the frames are maintained. Two testing frameworks for synthesizing a sequence with any number of frames are also proposed. The promising experimental results on piano music generation indicates the potential of the proposed framework in modeling other sequential data such as video.