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Found 5 papers, 0 papers with code
Balanced Multi-modal Federated Learning via Cross-Modal Infiltration
Federated learning (FL) underpins advancements in privacy-preserving distributed computing by collaboratively training neural networks without exposing clients' raw data.
Client-wise Modality Selection for Balanced Multi-modal Federated Learning
Selecting proper clients to participate in the iterative federated learning (FL) rounds is critical to effectively harness a broad range of distributed datasets.
Non-Exemplar Online Class-incremental Continual Learning via Dual-prototype Self-augment and Refinement
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.
DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning
Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones.
PMR: Prototypical Modal Rebalance for Multimodal Learning
Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to compensate for their inherent limitations.
