<ul><li>A novel framework has been proposed for aligning learnable latent spaces with arbitrary target distributions by using flow-based generative models as priors.</li><li>The method involves pretraining a flow model on target features to capture the distribution, which then regularizes the latent space through an alignment loss.</li><li>Minimizing this alignment loss establishes a computationally tractable surrogate objective for maximizing a variational lower bound on the log-likelihood of latents under the target distribution.</li><li>The proposed method aims to simplify the process by eliminating expensive likelihood evaluations and avoiding ODE solving during optimization, demonstrating its effectiveness through image generation experiments on ImageNet.</li></ul>

Aligning Latent Spaces with Flow Priors

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