Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration

1 code implementation • 22 Feb 2024 • Wonjeong Choi, Jungwuk Park, Dong-Jun Han, YoungHyun Park, Jaekyun Moon

In this paper, we propose consistency-guided temperature scaling (CTS), a new temperature scaling strategy that can significantly enhance the OOD calibration performance by providing mutual supervision among data samples in the source domains.

EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning

no code implementations • NeurIPS 2023 • Mohammad Mahdi Rahimi, Hasnain Irshad Bhatti, YoungHyun Park, Humaira Kousar, Jaekyun Moon

This global fitness vector is then disseminated back to the nodes, each of which applies the same update to be synchronized to the global model.

Federated Learning

Few-Round Learning for Federated Learning

no code implementations • NeurIPS 2021 • YoungHyun Park, Dong-Jun Han, Do-Yeon Kim, Jun Seo, Jaekyun Moon

Of central issues that may limit a widespread adoption of FL is the significant communication resources required in the exchange of updated model parameters between the server and individual clients over many communication rounds.

Federated Learning Few-Shot Learning