Graph Propagated Projection Unlearning: A Unified Framework for Vision and Audio Discriminative Models

TL;DR

This paper introduces GPPU, a scalable, modality-agnostic algorithm for class-level unlearning in deep neural networks, achieving significant speedups while maintaining model utility across vision and audio tasks.

Contribution

GPPU is a unified framework that efficiently unlearns class-specific information in vision and audio models using graph-based propagation and orthogonal projection.

Findings

Achieves 10-20x faster unlearning compared to previous methods.

Effectively removes class information while preserving performance on other classes.

Validated on six vision datasets and two audio benchmarks across various architectures.

Abstract

The need to selectively and efficiently erase learned information from deep neural networks is becoming increasingly important for privacy, regulatory compliance, and adaptive system design. We introduce Graph-Propagated Projection Unlearning (GPPU), a unified and scalable algorithm for class-level unlearning that operates across both vision and audio models. GPPU employs graph-based propagation to identify class-specific directions in the feature space and projects representations onto the orthogonal subspace, followed by targeted fine-tuning, to ensure that target class information is effectively and irreversibly removed. Through comprehensive evaluations on six vision datasets and two large-scale audio benchmarks spanning a variety of architectures including CNNs, Vision Transformers, and Audio Transformers, we demonstrate that GPPU achieves highly efficient unlearning, realizing 10-20x speedups over prior methodologies while preserving model utility on retained classes. Our framework provides a principled and modality-agnostic approach to machine unlearning, evaluated at a scale that has received limited attention in prior work, contributing toward more efficient and responsible deep learning.