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Therefore, we present a novel variational autoencoder approach to generate time series data on a probabilistic latent feature representation and enhance interpretability within the generative model ...

A variational autoencoder contains two components, an encoder and decoder. The encoder encodes any image into a latent space, with some tricks to ensure a smooth sampling distribution.

Mainstream lane detection methods often lack flexibility, accuracy, and efficiency in challenging scenarios, especially with occlusion and extreme lighting. To address this, we reframe lane detection ...

Exploring variational auto-encoder architectures, configurations, and datasets for generative music explainable AI Peer-Reviewed Publication Beijing Zhongke Journal Publising Co. Ltd.