cFlownet is a novel conditional generative model that is based on conditional normalizing flow (cFlow). The fundamental idea is to increase the expressivity of the cVAE by introducing a cFlow transformation step after the encoder. This yields improved approximations of the latent posterior distribution, allowing the model to capture richer segmentation variations. For more details about cFlownet model, refer to https://arxiv.org/abs/2006.02683.
The following table lists the cFlownet model supported by the Vitis AI Library.