SteeringDRL identifies two optimization regimes in diffusion autoencoders and uses gated residual U-Nets with a log SNR curriculum to steer training toward disentangled representations, improving performance across multiple benchmarks.
Learning disentangled representation by exploiting pretrained generative models: A contrastive learning view
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Steering Optimisation Trajectories in Diffusion Representation Learning
SteeringDRL identifies two optimization regimes in diffusion autoencoders and uses gated residual U-Nets with a log SNR curriculum to steer training toward disentangled representations, improving performance across multiple benchmarks.