Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Prediction

1 code implementation • CVPR 2022 • Sara Elkerdawy, Mostafa Elhoushi, Hong Zhang, Nilanjan Ray

On CIFAR, we reach similar accuracy to SOTA methods with 15% and 24% higher FLOPs reduction.

Binary Classification

To Filter Prune, or to Layer Prune, That Is The Question

1 code implementation • 11 Jul 2020 • Sara Elkerdawy, Mostafa Elhoushi, Abhineet Singh, Hong Zhang, Nilanjan Ray

LayerPrune presents a set of layer pruning methods based on different criteria that achieve higher latency reduction than filter pruning methods on similar accuracy.

Lightweight Monocular Depth Estimation Model by Joint End-to-End Filter pruning

1 code implementation • 13 May 2019 • Sara Elkerdawy, Hong Zhang, Nilanjan Ray

This is achieved by removing the least important features with a novel joint end-to-end filter pruning.

Monocular Depth Estimation

Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning

no code implementations • 23 Jan 2019 • Sara Elkerdawy, Nilanjan Ray, Hong Zhang

In addition, we achieve 95. 5% and 93. 19% on CompCars on both train-test splits, 70-30 and 50-50, outperforming the other methods by 4. 5% and 8% respectively.

Classification Fine-Grained Image Classification +3

Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges

no code implementations • 8 Jul 2017 • Mennatullah Siam, Sara Elkerdawy, Martin Jagersand, Senthil Yogamani

In this paper, the semantic segmentation problem is explored from the perspective of automated driving.

Segmentation Semantic Segmentation