Open Vocabulary Domain Unlearning

arXiv:2609.31356 · cs.CV, cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Accuracy — 26× in this paper · explained in 1 episode
• Generalization — 21× in this paper · explained in 13 episodes
• Targeted Manifold Scattering — 12× in this paper
• Embeddings — 11× in this paper · explained in 2 episodes
• Machine Unlearning — 11× in this paper · explained in 2 episodes
• Fisher Information — 10× in this paper · explained in 3 episodes
• Harmonic Mean — 10× in this paper
• Zero-Shot Generalization — 10× in this paper · explained in 5 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Robustness — 7× in this paper · explained in 5 episodes
• Open-Vocabulary Domain Unlearning — 6× in this paper
• Approximate Domain Unlearning — 5× in this paper
• Latent Space — 5× in this paper · explained in 7 episodes
• Catastrophic Forgetting — 4× in this paper · explained in 5 episodes
• Decoupling — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Maximum Mean Discrepancy — 4× in this paper
• Sample Efficiency — 4× in this paper · explained in 4 episodes
• Direct Preference Optimization — 3× in this paper
• Entanglement — 3× in this paper · explained in 3 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Negative Preference Optimization — 2× in this paper
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Approximate Unlearning — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Direct Preference Optimization (DPO) — 1× in this paper · explained in 9 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• MobileCLIP2: Improving Multi-Modal Reinforced Training
• SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
• Editing Models with Task Arithmetic
• Approximate Domain Unlearning for Vision-Language Models
• Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
• Unlearning with Fisher Masking
• Decoupled Weight Decay Regularization
• SEMU: Singular Value Decomposition for Efficient Machine Unlearning
• Sharpness-Aware Machine Unlearning
• Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning

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