Affiliation:
1. Independent Researcher, USA
2. Illinois Institute of Technology, USA
Abstract
The burgeoning complexity of contemporary deep learning models, while achieving unparalleled accuracy, has inadvertently introduced deployment challenges in resource-constrained environments. Through meticulous examination, the authors elucidate the critical determinants of successful distillation, including the architecture of the student model, the caliber of the teacher, and the delicate balance of hyperparameters. While acknowledging its profound advantages, they also delve into the complexities and challenges inherent in the process. The exploration underscores knowledge distillation's potential as a pivotal technique in optimizing the trade-off between model performance and deployment efficiency.
Reference26 articles.
1. Brand, Gholami, Horowitz, Zhou, & Bhabesh. (2022). Text classification for online conversations with machine learning on aws. AWS Machine Learning Blog.
2. Model compression
3. Chakraborty, Patel, & Salapaka. (2021). Recovery of power flow to critical infrastructures using mode-dependent droop-based inverters. arXiv preprint arXiv:2102.00046.
4. Learning to Ask: Neural Question Generation for Reading Comprehension
5. The trec-8 question answering track report;Trec,1999