Squeeze3D bridges latent spaces for extreme 3D compression without reconstruction losses
rishit_dagli · x · 2026-10-07
Rishit Dagli et al. (University of Toronto / Vector Institute, TMLR 2026) introduce Squeeze3D, which trains mapping networks to convert between latent spaces of different pretrained models via a unified latent, using only latent prediction loss plus a Gram/dimension-wise contrastive loss — no reconstruction-based losses.
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