Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction

TL;DR

This paper introduces Score-based Discriminator Correction (SBDC), a guidance method for improving noisy conditional diffusion models by using discriminator training, which enhances generation quality without retraining models.

Contribution

The paper proposes SBDC, a novel guidance technique that corrects noisy labels in diffusion models using adversarial discriminator training, improving performance efficiently.

Findings

SBDC outperforms previous methods in noisy label scenarios.

Limiting guidance to early generation stages improves results.

Method is computationally efficient and does not require retraining.

Abstract

Diffusion models have gained prominence as state-of-the-art techniques for synthesizing images and videos, particularly due to their ability to scale effectively with large datasets. Recent studies have uncovered that these extensive datasets often contain mistakes from manual labeling processes. However, the extent to which such errors compromise the generative capabilities and controllability of diffusion models is not well studied. This paper introduces Score-based Discriminator Correction (SBDC), a guidance technique for aligning noisy pre-trained conditional diffusion models. The guidance is built on discriminator training using adversarial loss, drawing on prior noise detection techniques to assess the authenticity of each sample. We further show that limiting the usage of our guidance to the early phase of the generation process leads to better performance. Our method is computationally efficient, only marginally increases inference time, and does not require retraining diffusion models. Experiments on different noise settings demonstrate the superiority of our method over previous state-of-the-art methods.