Latent Structure Emergence in Diffusion Models via Confidence-Based Filtering

2026-02-05

AI Summary

It remains unclear whether the initial noise latent space of diffusion models inherently encodes structured information predictive of generated sample attributes such as class labels. This work addresses this question by leveraging a pretrained classifier to assign confidence scores to unconditionally generated samples and subsequently isolating the corresponding initial noise vectors associated with high-confidence outputs. The study reveals, for the first time, that a class-discriminative structure emerges in the latent space only after filtering by classifier confidence. Experimental results demonstrate that this high-confidence noise subset substantially enhances class separability in the latent space, establishing a novel paradigm for guidance-free conditional generation that requires neither additional annotations nor architectural modifications to the underlying model.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Diffusion models rely on a high-dimensional latent space of initial noise seeds, yet it remains unclear whether this space contains sufficient structure to predict properties of the generated samples, such as their classes. In this work, we investigate the emergence of latent structure through the lens of confidence scores assigned by a pre-trained classifier to generated samples. We show that while the latent space appears largely unstructured when considering all noise realizations, restricting attention to initial noise seeds that produce high-confidence samples reveals pronounced class separability. By comparing class predictability across noise subsets of varying confidence and examining the class separability of the latent space, we find evidence of class-relevant latent structure that becomes observable only under confidence-based filtering. As a practical implication, we discuss how confidence-based filtering enables conditional generation as an alternative to guidance-based methods.

Problem

• diffusion models
• latent structure
• confidence scores
• class separability
• noise seeds

Innovation

• latent structure
• diffusion models
• confidence-based filtering
• class separability
• conditional generation