Spectral Saliency for Machine Unlearning

arXiv:2608.15548 · cs.LG, cs.AI · Submitted 2026-08-16 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-08-16

Updated: 2026-09-26

Code: https://github.com/mseitzer/pytorch-fid

Terminology

• Machine Unlearning — 19× in this paper · explained in 2 episodes
• Alignment — 15× in this paper · explained in 1 episode
• Diffusion Model — 11× in this paper · explained in 8 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Diffusion Models — 8× in this paper · explained in 6 episodes
• Continual Learning — 6× in this paper · explained in 2 episodes
• Model Utility — 6× in this paper
• Benchmark — 5× in this paper · explained in 2 episodes
• Approximate Unlearning — 4× in this paper · explained in 1 episode
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Forget Efficacy — 4× in this paper
• Spectral Saliency Unlearning — 4× in this paper
• U-net architecture — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Alignment Scores — 2× in this paper · explained in 1 episode
• Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
• Diffusion Process — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• alpha — 2× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Entanglement — 1× in this paper · explained in 3 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 9 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Influence Functions — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Stochastic Gradient Descent (SGD) — 1× in this paper · explained in 5 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Evaluating Machine Unlearning via Epistemic Uncertainty
• Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent
• Muon Optimizes Under Spectral Norm Constraints
• TOFU: A Task of Fictitious Unlearning for LLMs
• Llama 2: Open Foundation and Fine-Tuned Chat Models

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