Deep Learning Seminar / 25. Februar 2021, 10:00 – 11:00 Uhr
From Network Pruning to Network Architecture Search: A Unified Framework based on Group Sparsity
Referent: Avraam Chatzimichailidis (Fraunhofer ITWM)
[nur in Englisch verfügbar]
Network Pruning aims at reducing the neural network size with an acceptable egradation in the performance. Depending on the granularity, there are weight pruning, kernel pruning and filter pruning. The different pruning objectives are treated in literature separately, and various methods are proposed for each pruning objective. Another important topic in deep learning is network architecture search, which aims at finding a good architecture for the given problem at hand. In this seminar, we show how these seemingly unrelated problems can be effectively tackled under a unified framework based on group sparsity.