MemControl: Mitigating Memorization in Medical Diffusion Models via Automated Parameter Selection

MemControl: Mitigating Memorization in Medical Diffusion Models via Automated Parameter Selection

Abstract

Memorization in diffusion models poses critical privacy and ethical challenges, particularly when applied in sensitive fields like medical imaging. The paper "MemControl: Mitigating Memorization in Medical Diffusion Models via Automated Parameter Selection" addresses this issue by proposing a novel bi-level optimization framework designed to balance generation quality and memorization mitigation during the fine-tuning of diffusion models.

Introduction

Diffusion models have demonstrated exceptional capabilities in generating high-quality content across various modalities such as images, audio, and graphs. These models are gaining prominence in commercial applications and research, notably in domains requiring high-quality synthetic data generation. However, an intrinsic challenge persists: the tendency of these models to memorize training data, hence undermining data privacy and raising ethical concerns. This problem is particularly exacerbated in healthcare applications, where the disclosure of training data could lead to serious privacy violations.

Methodology

The proposed solution hypothesizes that the root cause of memorization lies in the overcapacity of deep neural networks. The authors assert that by regularizing model capacity, one can effectively mitigate memorization. Parameter-efficient fine-tuning (PEFT) methods provide an avenue to control model capacity by selectively updating specific subsets of parameters. However, identifying the optimal subset for balancing generation quality and memorization is complex and remains an open challenge.

To address this, the authors introduce a bi-level optimization framework. The inner loop fine-tunes the model assuming a given parameter subset specified by a binary mask, while the outer loop involves searching for the optimal mask based on memorization and generation quality metrics. The optimization targets a Pareto front to identify dominant configurations that minimize both memorization and quality degradation.

Experimental Setup

Experiments were carried out using the MIMIC medical imaging dataset, consisting primarily of chest X-rays and associated text. The primary evaluation metrics included:

• Fréchet Inception Distance (FID) to measure the generation quality.
• Nearest Neighbor Distance (AMD) and an Extraction Attack to quantify memorization.
• BioViL-T score to evaluate the alignment between generated images and text prompts.

The authors compared MemControl with baseline approaches such as full fine-tuning, standard PEFT methods like SV-DIFF and DiffFit, and existing mitigation strategies like Random Word Addition (RWA) and Threshold Mitigation.

Results

The experimental results underscore the efficacy of MemControl. Specifically, MemControl achieved superior performance compared to both standard PEFT methods and traditional mitigation techniques across all evaluation metrics. For instance, when compared with full fine-tuning, MemControl reduced the number of memorized images from 356 to 28 while maintaining high generation quality (lower FID score).

Furthermore, the masks learned using MemControl demonstrated robustness and transferability across different datasets. For instance, when applied to the Imagenette dataset, the masks originally optimized on MIMIC continued to yield commendable performance, suggesting that the discovered fine-tuning strategies can generalize across domains.

Implications and Speculations

The findings have significant implications for the deployment of diffusion models in sensitive applications. By effectively balancing memorization and generation quality, MemControl enables the responsible use of generative models in domains like healthcare, where data privacy is paramount. Moreover, the transferability of the optimization strategy suggests that once an optimal mask is found, it can be applied across various tasks, thereby reducing the computational burden associated with repeated searches.

Looking forward, further exploration into adaptive PEFT strategies for dynamic adjustment during fine-tuning could provide additional benefits. Integrating more complex memorization metrics or hybrid models that incorporate both training time and inference time interventions could also enhance the robustness of the approach.

Conclusion

The paper presents a sophisticated solution to a pressing problem in the deployment of diffusion models. By focusing on parameter-efficient fine-tuning and leveraging a bi-level optimization strategy, the authors offer a method that mitigates memorization while preserving generation quality. The success and transferability of the proposed approach underline its potential for broad application, offering a valuable tool for the responsible deployment of generative models in privacy-sensitive domains.