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/FAANG/GSM-pipeline
https://github.com/FAANG/proj-gs-meth
https://github.com/FL77N/RetinaNet-Based-on-PPdet
https://github.com/FPNAS/LBGAT
https://github.com/FaceOnLive/DeepFake-Detection-SDK-Linux
https://github.com/Faceplugin-ltd/FaceLivenessDetection-Android
https://github.com/FarihaHossain/SwinFSR
AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One
https://github.com/FayCai/framework
https://github.com/FelixHub/custom-LSEP-loss-function
ML-Net: multi-label classification of biomedical texts with deep neural networks
https://github.com/FelixMQuintana/Deep-Vasp-E
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/FenQQQ/Fusion-in-decoder
Distilling Knowledge from Reader to Retriever for Question Answering
https://github.com/FreddeFrallan/Multilingual-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/GXYM/Focal-loss
https://github.com/Gabriel-Macias/robust_frcnn
Automatic adaptation of object detectors to new domains using self-training
https://github.com/Gahyeonkim09/AAPL
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/GangmingZhao/GraphFPN-Graph-Feature-Pyramid-Network-for-Object-Detection
https://github.com/GeorgiosSmyrnis/multiclass_minimization_icml2020
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/Gitgigabyte/mmd
https://github.com/Gitgigabyte/mmdetection_
https://github.com/Guang000/Awesome-Dataset-Distillation/blob/main/README.md
https://github.com/Gugan0905/steel-defect-detection
https://github.com/Guillem96/efficient-net-tf
https://github.com/Guillem96/efficientdet-tf
https://github.com/HAOCHENYE/yehc_mmdet
https://github.com/HKUST-KnowComp/DISCOS-commonsense
Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset
https://github.com/Hao840/manifold-distillation
Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation
https://github.com/HarryHe11/CSD
https://github.com/HarryLui98/DMPEL
LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
https://github.com/Hazqeel09/ellzaf_ml
Understanding the Role of the Projector in Knowledge Distillation
https://github.com/HimBegginer/test_liver
https://github.com/Hitsaa/Practice
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/Hiwyl/mmdetection-obj
https://github.com/HollrayChan/gradcam-plus-plus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/HongSic/DarknetAI
https://github.com/HongshengHu/SIAs-Beyond_MIAs_in_Federated_Learning
FedMD: Heterogenous Federated Learning via Model Distillation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.