Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 11
- 보존된 평가 기록
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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://github.com/saeedmhz/Mechanical-MNIST-Crack-Path
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/sajjjadayobi/CLIPfa
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/salaniz/pycocoevalcap
https://github.com/salanueva/UniVSE
Evaluating Multimodal Representations on Visual Semantic Textual Similarity
https://github.com/salesforce/pb-ovd
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/salesforceairesearch/agentlite
BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents
https://github.com/salomonhotegni/mdmtn
https://github.com/salt-nlp/mic
The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems
https://github.com/samson6460/tf-keras-gradcamplusplus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/samson6460/tf.keras-gradcamplusplus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/samson6460/tf_keras_gradcamplusplus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/samsonadmin/modified-alexeyab-darknet
https://github.com/sandeepreddy06/fashion-mnist
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/sathishkumark27/mmdetection-icevision
https://github.com/satyakees/FaultNet
https://github.com/sayakpaul/FunMatch-Distillation
Knowledge distillation: A good teacher is patient and consistent
https://github.com/sberbank-ai/ru-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/scottjiao/Dynamic-selftraining-GCN
Effective Stabilized Self-Training on Few-Labeled Graph Data
https://github.com/scriddie/varsortability
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
https://github.com/scy-x/d3still
https://github.com/sdadas/polish-roberta
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/sdu2011/darknet_alexyab
https://github.com/securade/hub
https://github.com/see--/speech_recognition
https://github.com/semantic-web-company/ptlm_wsid
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/semitable/robotic-warehouse
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
https://github.com/seo3650/Contrastive_Representation_Distillation
https://github.com/seominseok0429/label-smoothing-visualization-pytorch
https://github.com/servicenow/workarena
https://github.com/seung-lab/znn-release
https://github.com/sfujim/BCQ
https://github.com/shamim-hussain/egt
https://github.com/shao-chi/ImageCaption
https://github.com/shaoeric/Peer-Collaborative-Learning-for-Online-Knowledge-Distillation
Peer Collaborative Learning for Online Knowledge Distillation
https://github.com/shaoshitong/NICO
NICO++: Towards Better Benchmarking for Domain Generalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.