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cs.CV2024
Distribution-Level Feature Distancing for Machine Unlearning: Towards a Better Trade-off Between Model Utility and Forgetting
Dasol Choi, Dongbin Na
With the explosive growth of deep learning applications and increasing privacy concerns, the right to be forgotten has become a critical requirement in various AI industries. For e…
cs.CV2023
Towards Reliable AI Model Deployments: Multiple Input Mixup for Out-of-Distribution Detection
Dasol Choi, Dongbin Na
Recent remarkable success in the deep-learning industries has unprecedentedly increased the need for reliable model deployment. For example, the model should alert the user if the…
cs.CV2023★ 3 cited
Towards Machine Unlearning Benchmarks: Forgetting the Personal Identities in Facial Recognition Systems
Dasol Choi, Dongbin Na
Machine unlearning is a crucial tool for enabling a classification model to forget specific data that are used in the training time. Recently, various studies have presented machin…