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Found 6 papers, 3 papers with code
Relaxed Contrastive Learning for Federated Learning
We propose a novel contrastive learning framework to effectively address the challenges of data heterogeneity in federated learning.
Multi-Level Branched Regularization for Federated Learning
A critical challenge of federated learning is data heterogeneity and imbalance across clients, which leads to inconsistency between local networks and unstable convergence of global models.
Communication-Efficient Federated Learning with Accelerated Client Gradient
To address this challenge, we propose a simple but effective federated learning framework, which improves the consistency across clients and facilitates the convergence of the server model.
Open-Set Representation Learning through Combinatorial Embedding
Visual recognition tasks are often limited to dealing with a small subset of classes simply because the labels for the remaining classes are unavailable.
Combinatorial Inference against Label Noise
Label noise is one of the critical sources that degrade generalization performance of deep neural networks significantly.
Learning to Optimize Domain Specific Normalization for Domain Generalization
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to individual domains.
Ranked #3 on
Unsupervised Domain Adaptation
on PACS
