SAGE adaptively augments high-loss embedding regions with UMAP-generated synthetic vectors and claims competitive distillation, though its average GLUE score (78.6) is below DistilBERT and MiniLM (79.4).
Semi-supervised knowledge transfer for deep learning from private training data.International Conference on Learning Represen- tations (ICLR), 2016
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Synthetic Adaptive Guided Embeddings (SAGE): A Novel Knowledge Distillation Method
SAGE adaptively augments high-loss embedding regions with UMAP-generated synthetic vectors and claims competitive distillation, though its average GLUE score (78.6) is below DistilBERT and MiniLM (79.4).