Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 11
- 보존된 평가 기록
- 90
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://github.com/Sivaneshmsc/FashionMNIST
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/SmartDataAnalytics/PyKEEN
https://github.com/Soft953/MedicalSegmentationDecathlon
https://github.com/SpikeKing/My-Bert
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/Stanch-LX/mmdetection
https://github.com/Stanford-AIMI/CheXagent
A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
https://github.com/StanfordVL/GibsonSim2RealCallenge
https://github.com/StanfordVL/GibsonSim2RealChallenge
https://github.com/StanfordVL/iGibsonChallenge2021
https://github.com/Stephenfang51/Focal_loss_turtorial
https://github.com/StoneGH/bert
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/Stsh4lson/TOM-2020
https://github.com/Suci2609/generate_train_list.py-
https://github.com/Sudhir11292rt/City-scale-Road-Audit
https://github.com/Sunnyzhr/habitat-lab
https://github.com/Superminionsfy/Personalized-Data-Generation-using-GAN
https://github.com/Sushma07/dancedarknet
https://github.com/SwarajPawar/Discrete-BCQ
https://github.com/TAMU-VITA/GAN-Slimming
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
https://github.com/TYtrack/FedNLP
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks
https://github.com/TakieddineSOUALHI/Transfer_learning
https://github.com/TangDL/mmdetection
https://github.com/TeaPoly/CTC-MWER
https://github.com/TeaPoly/CTC-OptimizedLoss
https://github.com/TeamLab/bert-gcn-for-paper-citation
A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks
https://github.com/Techyee/darknet_resource
https://github.com/TensorSpeech/TensorflowTTS
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/Text-to-Audio/AudioLCM
ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech
https://github.com/Thinklab-SJTU/CSL_RetinaNet_Tensorflow
https://github.com/Tony607/mmdetection
https://github.com/TonyX19/bert_hyperpartisan
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/TsingZ0/GFL
https://github.com/TsingZ0/PFL-Non-IID
https://github.com/UBCDingXin/cGAN-KD
https://github.com/UESTC-Liuxin/mmdetection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.