Group Sparsity: A Unified Framework for Network Pruning and Neural Architecture Search
| Title: | Group Sparsity: A Unified Framework for Network Pruning and Neural Architecture Search |
|---|---|
| Authors: | Chatzimichailidis, Avraam; Zela, Arber; Shalini, Shalini; Labus, Peter; Keuper, Janis; Hutter, Frank; Yang, Yang |
| Contributors: | IEEE, Computer Vision Foundation |
| Publication Year: | 2021 |
| Collection: | University of Applied Sciences: OPUS-HSO |
| Description: | We demonstrate how to exploit group sparsity in order to bridge the areas of network pruning and neural architecture search (NAS). This results in a new one-shot NAS optimizer that casts the problem as a single-level optimization problem and does not suffer any performance degradation from discretizating the architecture. |
| Document Type: | conference object |
| Language: | English |
| Availability: | https://opus.hs-offenburg.de/frontdoor/index/index/docId/5285 |
| Rights: | https://rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/closedAccess |
| Accession Number: | edsbas.A73CC7FD |
| Database: | BASE |