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/RichardChangCA/CSI_5386_NLP_Project
https://github.com/RichardMathewsII/YOLBO
https://github.com/Roll920/CURL
Neural Network Pruning with Residual-Connections and Limited-Data
https://github.com/Rongjiehuang/FastDiff
ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech
https://github.com/RuiminChen/centernet
https://github.com/RuoyuChen10/objectdetection-saliency-maps
https://github.com/SCPNet/Codes-for-SCPNet
https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection
https://github.com/SamuelGong/federated-learning-system-literature
Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies
https://github.com/SanaAwan5/FTLSimulation
https://github.com/SanketAgrawal/Catagorical-Focal-Loss
https://github.com/SatyamGaba/lt_od
https://github.com/Saurabh2805/kdd_cup_99
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives
https://github.com/ScandEval/ScandEval
ScandEval: A Benchmark for Scandinavian Natural Language Processing
https://github.com/SeHwanJoo/mmdetection_vinbig
https://github.com/SebasGarcia08/sku110-dense-object-mmdetection
https://github.com/SeventeenChen/fashionMNIST_CNN_master
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/SeventeenChen/fashionMNIST_LeNet_master
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/SeventeenChen/fashionMNIST_ResNet_master
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/SforAiDl/CountCLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ShaharrHorn/Detection-Game-logic
https://github.com/ShaojieJiang/tldr
https://github.com/ShashwatNigam99/MARBLER
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
https://github.com/Shi-qingyu/DeT
Decouple and Track: Benchmarking and Improving Video Diffusion Transformers for Motion Transfer
https://github.com/Shraddha2013/customyolo
https://github.com/Shraddha2013/darknett
https://github.com/Shraddha2013/darknetyol
https://github.com/Shraddha2013/darknetyolo
https://github.com/Shraddha2013/darknetyolo3
https://github.com/Shraddha2013/mydarknetyolo
https://github.com/Shraddha2013/myfileyolo
https://github.com/Sim-ai/mmdetection2
https://github.com/SimonThomine/DistillationAD
Anomaly Detection via Reverse Distillation from One-Class Embedding
https://github.com/SimonThomine/RememberingNormality
Anomaly Detection via Reverse Distillation from One-Class Embedding
https://github.com/SimonZsx/nasproj
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.