使用少量样本实现预训练模型的类级遗忘

Pravendra Singh, Pratik Mazumder, M. A. Karim
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引用次数: 1

摘要

为了解决现实世界的问题,深度学习模型在许多类上进行联合训练。然而,在未来,由于隐私/道德问题,一些类可能会受到限制,并且受限制的类知识必须从已对其进行培训的模型中删除。由于隐私/道德问题,可用的数据也可能有限,并且不可能重新训练模型。我们提出了一种新的方法来解决这个问题,而不影响模型对其余类别的预测能力。我们的方法识别与受限类高度相关的模型参数,并使用有限的可用训练数据从它们中删除有关受限类的知识。我们的方法明显更快,并且执行类似于在剩余类的完整数据上重新训练的模型。
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Attaining Class-level Forgetting in Pretrained Model using Few Samples
In order to address real-world problems, deep learning models are jointly trained on many classes. However, in the future, some classes may become restricted due to privacy/ethical concerns, and the restricted class knowledge has to be removed from the models that have been trained on them. The available data may also be limited due to privacy/ethical concerns, and re-training the model will not be possible. We propose a novel approach to address this problem without affecting the model's prediction power for the remaining classes. Our approach identifies the model parameters that are highly relevant to the restricted classes and removes the knowledge regarding the restricted classes from them using the limited available training data. Our approach is significantly faster and performs similar to the model re-trained on the complete data of the remaining classes.
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