Teacher Guided Training: An Efficient Framework for Knowledge Transfer

• URL: http://arxiv.org/abs/2208.06825v1
• Date: Sun, 14 Aug 2022 10:33:58 GMT
• Title: Teacher Guided Training: An Efficient Framework for Knowledge Transfer
• Authors: Manzil Zaheer, Ankit Singh Rawat, Seungyeon Kim, Chong You, Himanshu Jain, Andreas Veit, Rob Fergus, Sanjiv Kumar
• Abstract summary: We propose the teacher-guided training (TGT) framework for training a high-quality compact model. TGT exploits the fact that the teacher has acquired a good representation of the underlying data domain. We find that TGT can improve accuracy on several image classification benchmarks and a range of text classification and retrieval tasks.
• Score: 86.6784627427194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The remarkable performance gains realized by large pretrained models, e.g., GPT-3, hinge on the massive amounts of data they are exposed to during training. Analogously, distilling such large models to compact models for efficient deployment also necessitates a large amount of (labeled or unlabeled) training data. In this paper, we propose the teacher-guided training (TGT) framework for training a high-quality compact model that leverages the knowledge acquired by pretrained generative models, while obviating the need to go through a large volume of data. TGT exploits the fact that the teacher has acquired a good representation of the underlying data domain, which typically corresponds to a much lower dimensional manifold than the input space. Furthermore, we can use the teacher to explore input space more efficiently through sampling or gradient-based methods; thus, making TGT especially attractive for limited data or long-tail settings. We formally capture this benefit of proposed data-domain exploration in our generalization bounds. We find that TGT can improve accuracy on several image classification benchmarks as well as a range of text classification and retrieval tasks.

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