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

• URL: http://arxiv.org/abs/2604.13127v1
• Date: Mon, 13 Apr 2026 21:14:54 GMT
• Title: Graph Propagated Projection Unlearning: A Unified Framework for Vision and Audio Discriminative Models
• Abstract summary: We introduce Graph-Propagated Projection Unlearning (GPPU), a unified and scalable algorithm for class-level unlearning.<n>We demonstrate GPPU achieves highly efficient unlearning, realizing 10-20x speedups over prior methodologies.<n>Our framework provides a principled and modality-agnostic approach to machine unlearning, evaluated at a scale that has received limited attention in prior work.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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