  We tackle the problem of feature unlearning from a pre-trained image
generative model: GANs and VAEs. Unlike a common unlearning task where an
unlearning target is a subset of the training set, we aim to unlearn a specific
feature, such as hairstyle from facial images, from the pre-trained generative
models. As the target feature is only presented in a local region of an image,
unlearning the entire image from the pre-trained model may result in losing
other details in the remaining region of the image. To specify which features
to unlearn, we collect randomly generated images that contain the target
features. We then identify a latent representation corresponding to the target
feature and then use the representation to fine-tune the pre-trained model.
Through experiments on MNIST, CelebA, and FFHQ datasets, we show that target
features are successfully removed while keeping the fidelity of the original
models. Further experiments with an adversarial attack show that the unlearned
model is more robust under the presence of malicious parties.
