no code implementations • 23 Jun 2023 • Rishub Tamirisa, John Won, Chengjun Lu, Ron Arel, Andy Zhou
Recent advancements in federated learning (FL) seek to increase client-level performance by fine-tuning client parameters on local data or personalizing architectures for the local task.
no code implementations • 17 Jun 2021 • Rui Zhang, Chengjun Lu, Ziheng Jiao, Xuelong Li
In particular, in this paper, we apply AH to contrastive learning (AHCL) such that it can be effectively transferred from weak-supervised learning (given label priori) to unsupervised learning, where soft labels of contrastive learning are directly and adaptively learned.