Predicting quantized latent residuals (Latent Drift with FSQ) avoids identity collapse and noise interpolation, improving patient-specific 3D MRI neuro-forecasting over diffusion and autoregressive baselines.
(19) Consequently,f θ(xtest)is a dense vector on any bounded neighborhood of the training manifold, destroying thes-sparsity ofδ
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Progression as Latent Drift: Generative Forecasting of Slow-Evolving Pathologies
Predicting quantized latent residuals (Latent Drift with FSQ) avoids identity collapse and noise interpolation, improving patient-specific 3D MRI neuro-forecasting over diffusion and autoregressive baselines.