CIM directly aligns data distributions to condense large-scale datasets with minimal information loss, achieving new SOTA results on ImageNet-1K distillation at IPC=10.
Federated learning on heteroge- neous and long-tailed data via classifier re-training with federated features.arXiv preprint arXiv:2204.13399, 2022
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FedBB addresses inter-case, inter-class, and inter-client imbalances in federated learning via Positive Negative Balanced loss and Client Balanced Reweighting, outperforming baselines on X-ray and natural image datasets while using limited statistics for privacy.
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Condensing Large-Scale Datasets Directly with Minimal Information Loss
CIM directly aligns data distributions to condense large-scale datasets with minimal information loss, achieving new SOTA results on ImageNet-1K distillation at IPC=10.