Adversarial Self-Supervised Data-Free Distillation for Text Classification (2020.emnlp-main)
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| Challenge: | Existing knowledge distillation algorithms rely on the accessibility of the training dataset, which may be unavailable due to privacy issues. |
| Approach: | They propose a data-free distillation method for a pre-trained transformer-based model that uses plug & play Embedding Guessing to craft pseudo embeddings from the teacher's hidden knowledge. |
| Outcome: | The proposed method is the first data-free distillation framework designed for NLP tasks. |
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