18 citations · 19 across the 3 of their papers we have counts for
3 papers · 1 filter
Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Prediction
Sara Elkerdawy, Mostafa Elhoushi, Hong Zhang +1
Dynamic model pruning is a recent direction that allows for the inference of a different sub-network for each input sample during deployment. However, current dynamic methods rely…
To Filter Prune, or to Layer Prune, That Is The Question
Sara Elkerdawy, Mostafa Elhoushi, Abhineet Singh +2
Recent advances in pruning of neural networks have made it possible to remove a large number of filters or weights without any perceptible drop in accuracy. The number of parameter…
Lightweight Monocular Depth Estimation Model by Joint End-to-End Filter pruning
Sara Elkerdawy, Hong Zhang, Nilanjan Ray
Convolutional neural networks (CNNs) have emerged as the state-of-the-art in multiple vision tasks including depth estimation. However, memory and computing power requirements rema…