Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://gitlab.com/mia_jinns/pinn-multi-library-benchmark
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs
https://gitlab.com/secml/secml
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
https://huggingface.co/Open-Orca/Mistral-7B-SlimOrca
Orca: Progressive Learning from Complex Explanation Traces of GPT-4
https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B
Orca: Progressive Learning from Complex Explanation Traces of GPT-4
https://huggingface.co/datasets/Open-Orca/OpenOrca
Orca: Progressive Learning from Complex Explanation Traces of GPT-4
https://huggingface.co/julianzy/CheckGPT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.