Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/edisonlxy/detectiontool
https://github.com/edithal-14/A-Deep-Neural-Solution-To-Document-Level-Novelty-Detection-COLING-2018-
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
https://github.com/edwardclem/deepscribe
https://github.com/eify/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/einavyog/my-incremental-learning
https://github.com/eknim/gradcamplusplus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/elites2k19/prism-attack
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
https://github.com/elliottd/compareImageDescriptionMeasures
https://github.com/elsalmi/LendingClub
https://github.com/emanuelevivoli/CoMix
Comics Datasets Framework: Mix of Comics datasets for detection benchmarking
https://github.com/emi-group/evox
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning
https://github.com/emory-irlab/argumentation-rag
ConQRet: Benchmarking Fine-Grained Evaluation of Retrieval Augmented Argumentation with LLM Judges
https://github.com/ensta-u2is/torch-uncertainty
https://github.com/enzomuschik/distilfnd
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/epfl-dlab/multilingual-entity-insertion
https://github.com/epfml/collaborative-attention
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/eps696/aphantasia
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/eric-erki/darknet
https://github.com/eric-erki/yolov3
https://github.com/eric-vader/HD-BO-Additive-Models
NAS-Bench-101: Towards Reproducible Neural Architecture Search
https://github.com/ericchen321/ros_x_habitat
https://github.com/ericxumit/MGH_medical_AI
Multitask learning and benchmarking with clinical time series data
https://github.com/ericyinyzy/vlattack
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/erikbern/ann-benchmarks
ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms
https://github.com/ermuur/darknet
https://github.com/errec-sun/mnist
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/estrellaxyu/differential-alignment-for-daod
Align and Distill: Unifying and Improving Domain Adaptive Object Detection
https://github.com/etendue/habitat-api
https://github.com/eternal-f1ame/DaFKD2023
https://github.com/eth-pbl/tinyissimoyolo
Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO
https://github.com/ethz-asl/hf_net
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
https://github.com/ethz-asl/hfnet
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
https://github.com/eunanomist/PKD_BERT
https://github.com/evavanweenen/u-net
https://github.com/everything2x/realsr-ncnn-vulkan-python
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.