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f-VAEs: Improve VAEs with Conditional Flows
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In this paper, we integrate VAEs and flow-based generative models successfully and get f-VAEs. Compared with VAEs, f-VAEs generate more vivid images, solved the blurred-image problem of VAEs. Compared with flow-based models such as Glow, f-VAE is more lightweight and converges faster, achieving the same performance under smaller-size architecture.
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Cited by 2 Pith papers
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Latent Thermodynamic Flows: Unified Representation Learning and Generative Modeling of Temperature-Dependent Behaviors from Limited Data
LaTF combines state-predictive information bottleneck with normalizing flows and a temperature-steerable tilted Gaussian prior to infer free energy surfaces at unseen temperatures from simulation data at two temperatures.
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Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows
TarFlowLM models language in a continuous latent space with transformer-based autoregressive normalizing flows, using mixture-CDF and Rosenblatt couplings, and reports competitive NELBO on TEXT8 and OpenWebText.
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