DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning

no code implementations • 11 Mar 2024 • Sikai Bai, Jie Zhang, Shuaicheng Li, Song Guo, Jingcai Guo, Jun Hou, Tao Han, Xiaocheng Lu

Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target domain) is absent from the decentralized training data (source domains).

Domain Generalization Federated Learning +1

Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators

no code implementations • 11 Jul 2023 • Sikai Bai, Shuaicheng Li, Weiming Zhuang, Jie Zhang, Song Guo, Kunlin Yang, Jun Hou, Shuai Zhang, Junyu Gao, Shuai Yi

Theoretically, we show the convergence guarantee of the dual regulators.

Federated Learning

DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning

no code implementations • 2 May 2023 • Xiaocheng Lu, Ziming Liu, Song Guo, Jingcai Guo, Fushuo Huo, Sikai Bai, Tao Han

Compositional Zero-shot Learning (CZSL) aims to recognize novel concepts composed of known knowledge without training samples.

Compositional Zero-Shot Learning Novel Concepts +1

Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification

no code implementations • 12 Sep 2021 • Qi Wang, Sikai Bai, Junyu Gao, Yuan Yuan, Xuelong Li

In addition, due to domain gaps between different datasets, the performance is dramatically decreased when re-ID models pre-trained on label-rich datasets (source domain) are directly applied to other unlabeled datasets (target domain).

Person Re-Identification Unsupervised Domain Adaptation