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.
arXiv preprint arXiv:2301.07014 , year =
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LIVEditor-14B applies a new sparse attention method (ISA) that prunes context and uses query-sharpness routing to cut attention latency ~60% with no loss in editing quality on standard benchmarks.
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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.
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LIVEditor-14B: Lightning Unified Video Editing via In-Context Sparse Attention
LIVEditor-14B applies a new sparse attention method (ISA) that prunes context and uses query-sharpness routing to cut attention latency ~60% with no loss in editing quality on standard benchmarks.