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Multiclass Unlearning for Image Classification via Weight Filtering
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AI
DOI：10.1109/MIS.2024.3412742.png)
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Abstract
En 中文
Machine unlearning is an emerging paradigm for selectively removing the impact of training datapoints from a network. Unlike existing methods that target a limited subset or a single class, our framework unlearns all classes in a single round. We achieve this by modulating the network's components using memory matrices, enabling the network to demonstrate selective unlearning behavior for any class after training. By discovering weights that are specific to each class, our approach also recovers a representation of the classes which is explainable by design. We test the proposed framework on small- and medium-scale image classification datasets, with both convolution- and transformer-based backbones, showcasing the potential for explainable solutions through unlearning.
Keywords:
Data models
Training
Image classification
Computational modeling
Transformers
Intelligent systems
Vectors
Journal
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Citations:
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