Search Results for author: Yunfeng Fan

Found 7 papers, 2 papers with code

Deploying Foundation Model Powered Agent Services: A Survey

no code implementations18 Dec 2024 Wenchao Xu, Jinyu Chen, Peirong Zheng, Xiaoquan Yi, Tianyi Tian, Wenhui Zhu, Quan Wan, Haozhao Wang, Yunfeng Fan, Qinliang Su, Xuemin Shen

Foundation model (FM) powered agent services are regarded as a promising solution to develop intelligent and personalized applications for advancing toward Artificial General Intelligence (AGI).

model Model Compression +2

Detached and Interactive Multimodal Learning

1 code implementation28 Jul 2024 Yunfeng Fan, Wenchao Xu, Haozhao Wang, Junhong Liu, Song Guo

Recently, Multimodal Learning (MML) has gained significant interest as it compensates for single-modality limitations through comprehensive complementary information within multimodal data.

Transfer Learning

Balanced Multi-modal Federated Learning via Cross-Modal Infiltration

no code implementations31 Dec 2023 Yunfeng Fan, Wenchao Xu, Haozhao Wang, Jiaqi Zhu, Song Guo

Federated learning (FL) underpins advancements in privacy-preserving distributed computing by collaboratively training neural networks without exposing clients' raw data.

Distributed Computing Federated Learning +2

Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection

1 code implementation31 Dec 2023 Yunfeng Fan, Wenchao Xu, Haozhao Wang, Fushuo Huo, Jinyu Chen, Song Guo

On the other hand, we propose the modality selection aiming to select subsets of local modalities with great diversity and achieving global modal balance simultaneously.

Diversity Federated Learning +1

Non-Exemplar Online Class-incremental Continual Learning via Dual-prototype Self-augment and Refinement

no code implementations20 Mar 2023 Fushuo Huo, Wenchao Xu, Jingcai Guo, Haozhao Wang, Yunfeng Fan, Song Guo

In this paper, we propose a novel Dual-prototype Self-augment and Refinement method (DSR) for NO-CL problem, which consists of two strategies: 1) Dual class prototypes: vanilla and high-dimensional prototypes are exploited to utilize the pre-trained information and obtain robust quasi-orthogonal representations rather than example buffers for both privacy preservation and memory reduction.

Continual Learning

DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning

no code implementations14 Mar 2023 Yunfeng Fan, Wenchao Xu, Haozhao Wang, Jiaqi Zhu, Junxiao Wang, Song Guo

Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones.

class-incremental learning Class Incremental Learning +2

PMR: Prototypical Modal Rebalance for Multimodal Learning

no code implementations CVPR 2023 Yunfeng Fan, Wenchao Xu, Haozhao Wang, Junxiao Wang, Song Guo

Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to compensate for their inherent limitations.

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