Unleashing the Potential of Dataset Condensation: SRe^2L Achieves Record Accuracy on ImageNet-1K

In recent years, the spotlight has turned to data compression and distillation approaches, revolutionizing artificial intelligence research. These methods promise to efficiently represent large-scale datasets, enabling faster model training, cost-effective data storage, and preservation of vital information. However, existing solutions have struggled to compress high-resolution datasets like ImageNet-1K due to formidable computational overheads. A research […]

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