MaskAlign uses random token-subset alignment and pre-mask mixing to reduce diffusion models' reliance on complete clean-image token sets during representation alignment.
arXiv preprint arXiv:2505.16792 (2025) 4
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Matching in semantic SSL feature space via Sinkhorn divergence enables effective one-step generation on ImageNet by inducing compact geometry for distribution matching, with training and evaluation features best kept distinct.
Attention Separation ablations show that gains from SRA to Self-Flow in diffusion transformers arise mainly from noise-dimension data augmentation rather than token-level self-supervision.
Aligning flow-matching assembly networks to frozen 3D encoder topology via cosine and CKA losses speeds training up to 6.9× and improves in- and out-of-distribution assembly accuracy with zero inference cost.
citing papers explorer
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MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training
MaskAlign uses random token-subset alignment and pre-mask mixing to reduce diffusion models' reliance on complete clean-image token sets during representation alignment.
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Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation
Matching in semantic SSL feature space via Sinkhorn divergence enables effective one-step generation on ImageNet by inducing compact geometry for distribution matching, with training and evaluation features best kept distinct.
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From SRA to Self-Flow: Data Augmentation or Self-Supervision?
Attention Separation ablations show that gains from SRA to Self-Flow in diffusion transformers arise mainly from noise-dimension data augmentation rather than token-level self-supervision.
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TORA: Topological Representation Alignment for 3D Shape Assembly
Aligning flow-matching assembly networks to frozen 3D encoder topology via cosine and CKA losses speeds training up to 6.9× and improves in- and out-of-distribution assembly accuracy with zero inference cost.