no code implementations • 24 Nov 2022 • Yu-Tong Cao, Jingya Wang, Baosheng Yu, DaCheng Tao
To further enhance the active learner via large-scale unlabelled data, we introduce multiple peer students into the active learner which is trained by a novel learning paradigm, including the In-Class Peer Study on labelled data and the Out-of-Class Peer Study on unlabelled data.
2 code implementations • ICCV 2023 • Yu-Tong Cao, Ye Shi, Baosheng Yu, Jingya Wang, DaCheng Tao
In this paper, we propose a federated active learning paradigm to efficiently learn a global model with limited annotation budget while protecting data privacy in a decentralized learning way.
1 code implementation • ECCV 2020 • Yu-Tong Cao, Jingya Wang, DaCheng Tao
The current state-of-the-art methods either focus on learning better cross-modal embeddings by mining only seen data, or they explicitly use generative adversarial networks (GANs) to synthesize unseen features.