STARFISH: faST Accuracy Recovery in pruned networks From Internal State Healing
Pruning is a process designed to reduce the number of weights in a large neural network. This can substantially speed up inference but might cause a considerable reduction in the model's accuracy, and thus it is usually followed by a healing process that regains some of the lost accuracy. In this paper, we propose a new healing method, STARFISH, that can recover (most of) the accuracy of any pruned network efficiently. The main idea of STARFISH is to optimize the pruned network to align with the original network's internal state representations using a tiny calibration set of unlabeled examples. For the common case of removing 50% of the weights, STARFISH healing improves the recovered ac...
| Authors | Shir Maon, Odelia Melamed, Adi Shamir |
|---|---|
| Primary category | cs.LG |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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