ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks

The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling parameter spaces, offering a unified strategy for handling continuous, discretized, and discrete data. However, BFNs cannot learn high-level semantic representation from the parameter space since common encoders, which encode data into one static representation, cannot capture semantic changes in parameters. This motivates a new direction: learning semantic representations hidden in the parameter spaces to characterize mixed-typed noisy data. Accordingly, we propose a representation learning framework named ParamReL, which operates in the parameter space to obtain parameter-wise latent semantics that exhibit progressive structures. Specifically, ParamReL proposes a self-encoder to learn latent semantics directly from parameters, rather than from observations. The encoder is then integrated into BFNs, enabling representation learning with various formats of observations. Mutual information terms further promote the disentanglement of latent semantics and capture meaningful semantics simultaneously. We illustrate conditional generation and reconstruction in ParamReL via expanding BFNs, and extensive quantitative experimental results demonstrate the superior effectiveness of ParamReL in learning parameter representation.