3 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
KoMultiText: Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services
Dasol Choi, Jooyoung Song, Eunsun Lee +3
With the growth of online services, the need for advanced text classification algorithms, such as sentiment analysis and biased text detection, has become increasingly evident. The…