Randomized layer shuffling and column-wise sign flips make interfering task deltas nearly orthogonal, allowing one compressed superset to retrieve near-fine-tuned accuracy for many tasks.
An analysis of single-layer networks in unsupervised feature learning
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RanDeS: Randomized Delta Superposition for Multi-Model Compression
Randomized layer shuffling and column-wise sign flips make interfering task deltas nearly orthogonal, allowing one compressed superset to retrieve near-fine-tuned accuracy for many tasks.