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Teacher Guided Training: An Efficient Framework for Knowledge Transfer

Chong You
Himanshu Jain
Rob Fergus
International Conference on Learning Representations (2023) (to appear)
Google Scholar


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 devise teacher-guided training (TGT) framework for training a high-quality compact model that leverages the knowledge acquired by pre-trained \emph{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 ambient space. Furthermore, we can use the teacher to explore the instance 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. Among our empirical evaluations, we find that TGT can improve accuracy on ImageNet-LT by 10% compared to natural baseline and match accuracy on sentiment analysis on Amazon reviews without the need for pretraining.

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