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Found 5 papers, 1 papers with code
Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration
In this paper, we propose consistency-guided temperature scaling (CTS), a new temperature scaling strategy that can significantly enhance the OOD calibration performance by providing mutual supervision among data samples in the source domains.
Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization
In domain generalization (DG), the target domain is unknown when the model is being trained, and the trained model should successfully work on an arbitrary (and possibly unseen) target domain during inference.
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries
While federated learning (FL) allows efficient model training with local data at edge devices, among major issues still to be resolved are: slow devices known as stragglers and malicious attacks launched by adversaries.
FedMes: Speeding Up Federated Learning with Multiple Edge Servers
Our key idea is to utilize the devices located in the overlapping areas between the coverage of edge servers; in the model-downloading stage, the devices in the overlapping areas receive multiple models from different edge servers, take the average of the received models, and then update the model with their local data.
Sself: Robust Federated Learning against Stragglers and Adversaries
While federated learning allows efficient model training with local data at edge devices, two major issues that need to be resolved are: slow devices known as stragglers and malicious attacks launched by adversaries.
