A comprehensive survey on model compression and acceleration

By Tejalal Choudhary, Vipul Kumar Mishra, Anurag Goswami, Sarangapani Jagannathan. Artificial Intelligence Review, 2020

519 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7fd582680ee61f6333a23bd0374f05cd6fd3dcb4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tailin Liang
2. C. John Glossner
3. Shaobo Shi
4. Lei Wang
5. Elias Frantar
6. Dan Alistarh
7. Lingao Xiao
8. Yang He

In Computer Vision and Pattern Recognition

1. Tailin Liang
2. C. John Glossner
3. Shaobo Shi
4. Lei Wang
5. Elias Frantar
6. Dan Alistarh
7. Lingao Xiao
8. Yang He

Citations in the corpus

• A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations (2023, unknown): “O VER the past several years, Deep Neural Networks ( DNNs ) have achieved conspicuous progress in various domains and applications, such as Computer Vision ( CV ) [1], [2], Natural Language Processing ( NLP ) [3], [4], Audio Signal Processing ( ASP ) [5], [6], and cross-modal applications [7], [8].”
• Pruning and Quantization for Deep Neural Network Acceleration: A Survey (2021, methodology): “BN could also raise the performance of the clusterbased quantize technique by reducing parameter dependency [46].”