Abstract: The variational autoencoder (VAE) has proven highly effective in monitoring nonlinear stochastic processes, primarily under the assumption of complete and temporally independent data.

This is the PyTorch version of CT-VAE & CT-CAVAE. If you find any issues, please let me know: jung@m.scnu.edu.cn. If you feel our work has been helpful, thank you for the citation.

Important Note: This repository implements SVG-T2I, a text-to-image diffusion framework that performs visual generation directly in Visual Foundation Model (VFM) representation space, rather than ...

Abstract: Aerospace self-lubricating bearings are critical components in aircraft transmission systems, where wearinduced degradation under high-load and dynamic conditions poses significant ...