EGA adapter induces gradient sparsity so that 96.5% of triplets become inactive at convergence, preserving unseen-class geometry while refining seen classes and improving worst-case Label Precision on OOD benchmarks.
Achieving low-latency graph-based vector search via aligning best- first search algorithm with ssd
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EGA: Adapting Frozen Encoders for Vector Search with Bounded Out-of-Distribution Degradation
EGA adapter induces gradient sparsity so that 96.5% of triplets become inactive at convergence, preserving unseen-class geometry while refining seen classes and improving worst-case Label Precision on OOD benchmarks.