Concept forgetting via label annealing
Keywords: Concept forgetting, Privacy, Bias, Computer Vision (CV)
Abstract: The effectiveness of current machine learning models relies on their ability to grasp diverse concepts present in datasets. However, biased and noisy data can inadvertently cause these models to be biased toward certain concepts, undermining their ability to generalize and provide utility. Consequently, modifying a trained model to forget these concepts becomes imperative for their responsible deployment. We refer to this problem as *concept forgetting*. Our goal is to develop techniques for forgetting specific undesired concepts from a pre-trained classification model's prediction. To achieve this goal, we present an algorithm called **L**abel **AN**nealing (**LAN**). This iterative algorithm employs a two-stage method for each iteration. In the first stage, pseudo-labels are assigned to the samples by annealing or redistributing the original labels based on the current iteration's model predictions of all samples in the dataset. During the second stage, the model is fine-tuned on the dataset with pseudo-labels. We illustrate the effectiveness of the proposed algorithms across various models and datasets. Our method reduces *concept violation*, a metric that measures how much the model forgets specific concepts, by about 85.35\% on the MNIST dataset, 73.25\% on the CIFAR-10 dataset, and 69.46\% on the CelebA dataset while maintaining high model accuracy. Our implementation can be found at this following link: \url{https://anonymous.4open.science/r/LAN-141B/}
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 9880
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