CoReDi coevolves semantic representations with the diffusion model via a jointly learned linear projection stabilized by stop-gradient, normalization, and regularization, yielding faster convergence and higher sample quality than fixed-representation baselines.
Advances in neural information processing systems33, 6840–6851 (2020) 2
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Drift-AR achieves 3.8-5.5x speedup in AR-diffusion image models by using entropy to enable entropy-informed speculative decoding and single-step (1-NFE) anti-symmetric drifting decoding.
LDNLM replaces the quadratic similarity and averaging steps of nonlocal means with deep convolutional feature extraction and linear attention operations to produce a linear-complexity denoiser for multiplicative noise that retains traditional NLM interpretability.
citing papers explorer
-
Coevolving Representations in Joint Image-Feature Diffusion
CoReDi coevolves semantic representations with the diffusion model via a jointly learned linear projection stabilized by stop-gradient, normalization, and regularization, yielding faster convergence and higher sample quality than fixed-representation baselines.
-
Drift-AR: Single-Step Visual Autoregressive Generation via Anti-Symmetric Drifting
Drift-AR achieves 3.8-5.5x speedup in AR-diffusion image models by using entropy to enable entropy-informed speculative decoding and single-step (1-NFE) anti-symmetric drifting decoding.
-
Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removal
LDNLM replaces the quadratic similarity and averaging steps of nonlocal means with deep convolutional feature extraction and linear attention operations to produce a linear-complexity denoiser for multiplicative noise that retains traditional NLM interpretability.