對齊潛在空間與流動先驗

摘要

本文提出了一種新穎的框架，通過利用基於流的生成模型作為先驗，將可學習的潛在空間對齊到任意目標分佈。我們的方法首先在目標特徵上預訓練一個流模型，以捕捉底層分佈。這個固定的流模型隨後通過對齊損失來正則化潛在空間，該對齊損失重新表述了流匹配目標，將潛在變量視為優化目標。我們正式證明，最小化這個對齊損失建立了一個計算上易處理的替代目標，用於最大化目標分佈下潛在變量的對數似然的變分下界。值得注意的是，所提出的方法消除了計算昂貴的似然評估，並在優化過程中避免了ODE求解。作為概念驗證，我們在受控環境中展示了對齊損失的景觀密切近似於目標分佈的負對數似然。我們進一步通過在ImageNet上進行大規模圖像生成實驗，並伴隨詳細的討論和消融研究，驗證了我們方法的有效性。通過理論和實證的雙重驗證，我們的框架為潛在空間對齊開闢了一條新途徑。

English

This paper presents a novel framework for aligning learnable latent spaces to arbitrary target distributions by leveraging flow-based generative models as priors. Our method first pretrains a flow model on the target features to capture the underlying distribution. This fixed flow model subsequently regularizes the latent space via an alignment loss, which reformulates the flow matching objective to treat the latents as optimization targets. We formally prove that 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. Notably, the proposed method eliminates computationally expensive likelihood evaluations and avoids ODE solving during optimization. As a proof of concept, we demonstrate in a controlled setting that the alignment loss landscape closely approximates the negative log-likelihood of the target distribution. We further validate the effectiveness of our approach through large-scale image generation experiments on ImageNet with diverse target distributions, accompanied by detailed discussions and ablation studies. With both theoretical and empirical validation, our framework paves a new way for latent space alignment.

對齊潛在空間與流動先驗

Aligning Latent Spaces with Flow Priors

摘要

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