1 code implementation • ICCV 2023 • Zexi Li, Xinyi Shang, Rui He, Tao Lin, Chao Wu
Recent advances in neural collapse have shown that the classifiers and feature prototypes under perfect training scenarios collapse into an optimal structure called simplex equiangular tight frame (ETF).
no code implementations • 4 Mar 2023 • Xinyi Shang, Gang Huang, Yang Lu, Jian Lou, Bo Han, Yiu-ming Cheung, Hanzi Wang
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data.
1 code implementation • 14 Feb 2023 • Zexi Li, Tao Lin, Xinyi Shang, Chao Wu
In federated learning (FL), weighted aggregation of local models is conducted to generate a global model, and the aggregation weights are normalized (the sum of weights is 1) and proportional to the local data sizes.
1 code implementation • 30 Apr 2022 • Xinyi Shang, Yang Lu, Yiu-ming Cheung, Hanzi Wang
Federated learning provides a privacy guarantee for generating good deep learning models on distributed clients with different kinds of data.
2 code implementations • 28 Apr 2022 • Xinyi Shang, Yang Lu, Gang Huang, Hanzi Wang
Experiments on several benchmark datasets show that the proposed CReFF is an effective solution to obtain a promising FL model under heterogeneous and long-tailed data.