Teacher-Class Network: A Neural Network Compression Mechanism

| October 15, 2021 | 0 Comments

Shaiq Munir Malik, Fnu Mohbat, Muhammad Umair Haider, Muhammad Musab Rasheed and Murtaza Taj

Abstract:

To reduce the overwhelming size of Deep Neural Networks, teacher-student techniques aim to transfer knowledge from a complex teacher network to a simple student network. We instead propose a novel method called the teacher-class network consisting of a single teacher and multiple student networks (class of students). Instead of transferring knowledge to one student only, the proposed method divides learned space into sub-spaces, and each sub-space is learned by a student. Our students are not trained for problem-specific logits; they are trained to mimic knowledge (dense representation) learned by the teacher network; thus, the combined knowledge learned by the class of students can be used to solve other problems. The proposed teacher-class architecture is evaluated on several benchmark datasets such as MNIST, Fashion MNIST, IMDB Movie Reviews, CIFAR-10, and ImageNet on multiple tasks such as image and sentiment classification. Our approach outperforms the state-of-the-art single student approach in terms of accuracy and computational cost while achieving a 10-30 times reduction in parameters. Code is available at Github.

Resources
PDF: Paper
Code: Github
Video Presentation: BMVC Presentation

Text Reference:

Shaiq Munir Malik, Fnu Mohbat, Muhammad Umair Haider, Muhammad Musab Rasheed and Murtaza Taj, 
"Teacher-Class Network: A Neural Network Compression Mechanism," 
The 32nd British Machine Vision Conference (BMVC), 2021.

Bibtex Reference:

@INPROCEEDINGS{TajBMVC2022,
  author={Shaiq Munir Malik, Fnu Mohbat, Muhammad Umair Haider, Muhammad Musab Rasheed and Murtaza Taj},
  booktitle={The 32nd British Machine Vision Conference (BMVC)}, 
  title={Teacher-Class Network: A Neural Network Compression Mechanism}, 
  year={2021},
}

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Category: Conference Papers, Publications

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