Model Compression with Generative Adversarial Networks

By Ruishan Liu, Nicoló Fusi, Lester W. Mackey. 2018

10 citations recorded by the source corpus.

How researchers see this paper

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

1. Dacheng Tao
2. Jianping Gou
3. Baosheng Yu
4. S. Maybank
5. Suman V. Ravuri
6. Oriol Vinyals
7. Quoc V. Le
8. Yoshua Bengio

Citations in the corpus

• Knowledge Distillation: A Survey (2020, methodology): “Furthermore, knowledge distillation can be flexibly deployed to various excellent learning schemes, such as the adversarial learning (Liu et al., 2018), auto machine learning (Macko et al.”
• Classification Accuracy Score for Conditional Generative Models (2019, methodology): “Finally, [36] classifies real data using a student network mimicking a teacher network pretrained on real data but distilled on GAN data.”
• Compressing GANs using Knowledge Distillation (2019, unknown): “There has been recent progress in the area of neural network compression (Liu et al., 2019) (Ba & Caurana, 2013) (Urban et al., 2017) and various compression techniques have been exercised to solve this problem (Belagiannis et al., 2018) (Xu et al., 2017) (Yim et al., 2017) (Kim & Kim, 2017).”