擦除CLIP记忆：非破坏性、无数据零样本类去学习在CLIP模型中
Erasing CLIP Memories: Non-Destructive, Data-Free Zero-Shot class Unlearning in CLIP Models
AI总结：
本文提出了一种基于空空间投影的非破坏性零样本类去学习方法，有效降低CLIP模型中目标类别的性能，同时保留整体多模态知识。
摘要：
我们介绍了一种新的闭式方法，用于多模态模型中的选择性去学习，特别是针对预训练模型，如CLIP。我们的方法利用空空间投影来擦除最终投影层中嵌入的目标类信息，无需任何重新训练或使用遗忘集中的图像。通过计算由目标文本嵌入张成的子空间的正交基，并将这些方向投影，我们显著减少了图像特征与不希望的类别的对齐。与传统去学习技术依赖于迭代微调和大量数据整理不同，我们的方法在计算上高效且精确。这导致目标类别的零样本性能显著下降，同时保持模型的整体多模态知识。我们的实验表明，即使部分投影也能在完全去学习和保留有用信息之间取得平衡，解决了模型去污染和隐私保护的关键挑战。
英文摘要：
We introduce a novel, closed-form approach for selective unlearning in multimodal models, specifically targeting pretrained models such as CLIP. Our method leverages nullspace projection to erase the target class information embedded in the final projection layer, without requiring any retraining or the use of images from the forget set. By computing an orthonormal basis for the subspace spanned by target text embeddings and projecting these directions, we dramatically reduce the alignment between image features and undesired classes. Unlike traditional unlearning techniques that rely on iterative fine-tuning and extensive data curation, our approach is both computationally efficient and surgically precise. This leads to a pronounced drop in zero-shot performance for the target classes while preserving the overall multimodal knowledge of the model. Our experiments demonstrate that even a partial projection can balance between complete unlearning and retaining useful information, addressing key challenges in model decontamination and privacy preservation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
