Introduces the Invariant Contamination Ratio (ICR), a Fisher-based metric, to evaluate how diffusion models balance invariant representations with residual variation and to detect the onset of memorization during training.
MCLR: Improving Conditional Modeling via Inter-Class Likelihood-Ratio Maximization and Unifying Classifier-Free Guidance with Alignment Objectives
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Diffusion models achieve strong performance in generative modeling, but their success often relies heavily on classifier-free guidance (CFG), an inference-time heuristic that modifies the sampling trajectory. In theory, diffusion models trained with standard denoising score matching (DSM) should recover the target data distribution, raising two fundamental questions: (i) why is inference-time guidance necessary in practice, and (ii) can its underlying effect be internalized into a principled training objective? In this work, we argue that a key limitation of standard DSM is insufficient inter-class separation. To address this issue, we propose MCLR, an alignment objective that explicitly maximizes inter-class likelihood-ratios during training. Fine-tuning diffusion models with MCLR induces CFG-like improvements under standard sampling, substantially improving guidance-free conditional generation and narrowing the gap to inference-time CFG. Beyond these empirical benefits, we show theoretically that the CFG-guided score is exactly the optimal solution to a sample-adaptive weighted MCLR objective. This result connects CFG to alignment-based objectives, providing a mechanistic interpretation of CFG as an implicit inference-time contrastive alignment procedure.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
ForcingDAS is a diffusion-based data assimilation framework that learns joint-trajectory priors to unify filtering and smoothing while reducing error accumulation on non-Markovian observations.
citing papers explorer
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Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles
Introduces the Invariant Contamination Ratio (ICR), a Fisher-based metric, to evaluate how diffusion models balance invariant representations with residual variation and to detect the onset of memorization during training.
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ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing
ForcingDAS is a diffusion-based data assimilation framework that learns joint-trajectory priors to unify filtering and smoothing while reducing error accumulation on non-Markovian observations.