Papers with Code 아카이브
Knowledge Distillation
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- 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/mcanalesmayo/heterogeneous-slambench
Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM
https://github.com/mcg-nju/adamixer
https://github.com/mckunkel/DistillingObjectDetector
https://github.com/mdv3101/darknet-yolov3
https://github.com/media2x/realsr-ncnn-vulkan-python
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
https://github.com/megagonlabs/ambignlg
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/mengfu188/mmdetection.bak
https://github.com/mertyg/post-hoc-cbm
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/metaevo/metabox
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
https://github.com/metodj/anytimeclassification
https://github.com/mgbellemare/Arcade-Learning-Environment
The Arcade Learning Environment: An Evaluation Platform for General Agents
https://github.com/mi2-warsaw/MI2DataLab_Seminarium
Tunability: Importance of Hyperparameters of Machine Learning Algorithms
https://github.com/mic-dkfz/lesionlocator
https://github.com/mic-dkfz/medicaldetectiontoolkit
https://github.com/miccunifi/searle
iSEARLE: Improving Textual Inversion for Zero-Shot Composed Image Retrieval
https://github.com/michael-hoss/rolling-review-updates
A Review of Testing Object-Based Environment Perception for Safe Automated Driving
https://github.com/michaelaerni/interpolation_robustness
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/michal-crosta/GIT19
https://github.com/michi-3000/eyeclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/microsoft/AirSim-Drone-Racing-Lab
https://github.com/microsoft/GLIP
Evaluating Large-Vocabulary Object Detectors: The Devil is in the Details
https://github.com/microsoft/MSMARCO-Conversational-Search
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Document-Ranking
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-OpenKP
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Passage-Ranking
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Question-Answering
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MT-DNN
https://github.com/microsoft/Multilingual-Evaluation-of-Generative-AI-MEGA
https://github.com/microsoft/OpenKP
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/TREC-2019-Deep-Learning
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/ar2
https://github.com/microsoft/bitblas
BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation
https://github.com/microsoft/klite
K-LITE: Learning Transferable Visual Models with External Knowledge
https://github.com/microsoft/personalizedfl
https://github.com/microsoft/unicl
K-LITE: Learning Transferable Visual Models with External Knowledge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.