The authors tackle fair comparison in diffusion-based out-of-distribution (OOD) detection by introducing a standardized evaluation protocol (MBE: Mutualized Backbone-Equated) that aligns corruption levels and test-time cost across different diffusion backbones. They then propose Canonical Feature Snapshots (CFS), a family of OOD detectors that probe a frozen diffusion model using only a tiny number of internal activations at low-noise timesteps, without running full denoising trajectories. The strongest one-forward variant (CFS(1x2)) and an even smaller decoder-only variant remain highly competitive, showing that most of the OOD signal in diffusion models is concentrated in sparse internal states rather than requiring expensive full sampling.
Main takeaways:
- MBE protocol standardizes comparisons across diffusion OOD methods by aligning corruption levels and test-time compute budgets
- CFS detectors extract OOD signals from a frozen diffusion backbone using only a few internal activations at low-noise timesteps, not full denoising
- The strongest one-forward variant (CFS(1x2)) and a decoder-only variant are competitive, showing OOD information is concentrated in sparse internal states
- Local diagnostic theory explains this through conditional encoder-decoder complementarity, diagonal-score separation, and low-noise stability