GSTDS selects a scheduled fraction of each training batch via Fiedler-vector ranking and reference-model losses, reporting comparable or better accuracy on three small image benchmarks with up to 4x fewer training FLOPs, but with single-run results and an internal algorithm mismatch.
Neural computation 15(6), 1373–1396 (2003)
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Efficient Training of Deep Networks using Guided Spectral Data Selection: A Step Toward Learning What You Need
GSTDS selects a scheduled fraction of each training batch via Fiedler-vector ranking and reference-model losses, reporting comparable or better accuracy on three small image benchmarks with up to 4x fewer training FLOPs, but with single-run results and an internal algorithm mismatch.