Diffusion-based representation learning has achieved substantial attention
due to its promising capabilities in latent representation and sample
generation. Recent studies have employed an auxiliary encoder to identify a
corresponding representation from a sample and to adjust the dimensionality of
a latent variable z. Meanwhile, this auxiliary structure invokes information
split problem because the diffusion and the auxiliary encoder would divide the
information from the sample into two representations for each model.
Particularly, the information modeled by the diffusion becomes over-regularized
because of the static prior distribution on xT. To address this problem, we
introduce Diffusion Bridge AuteEncoders (DBAE), which enable z-dependent
endpoint xT inference through a feed-forward architecture. This structure
creates an information bottleneck at z, so xT becomes dependent on z in its
generation. This results in two consequences: 1) z holds the full information
of samples, and 2) xT becomes a learnable distribution, not static any further.
We propose an objective function for DBAE to enable both reconstruction and
generative modeling, with their theoretical justification. Empirical evidence
supports the effectiveness of the intended design in DBAE, which notably
enhances downstream inference quality, reconstruction, and disentanglement.
Additionally, DBAE generates high-fidelity samples in the unconditional
generation.

通过引入 Diffusion Bridge AuteEncoders（DBAE），我们解决了传统扩散模型中信息拆分问题，并实现了更好的重构模型和生成模型，提高了下游推断质量、重构质量和解耦性能。