Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/Orange-OpenSource/carla_croco_vcnet
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
https://github.com/PAIR-code/lit
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/PaddlePaddle/PASSL/blob/main/docs/Train_CLIP_model.md
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/PaddlePaddle/PGL
https://github.com/PaddlePaddle/PGL/tree/main/ogb_examples/graphproppred/ogbg_molpcba
https://github.com/PaddlePaddle/PGL/tree/main/ogb_examples/nodeproppred/ogbn-arxiv/unimp_appnp_vnode_smooth
https://github.com/PaddlePaddle/PGL/tree/main/ogb_examples/nodeproppred/ogbn-proteins/unimp_cross_feat
https://github.com/PaddlePaddle/PGL/tree/master/examples/GaAN
https://github.com/PaddlePaddle/PGL/tree/master/ogb_examples/nodeproppred/ogbn-arxiv
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleFL
https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/distilbert/modeling.py
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/tinybert
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/PaddlePaddle/PaddleScience
https://github.com/PaddlePaddle/PaddleSpeech
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/PathologyDataScience/NuCLS
https://github.com/Pavyel/Novelty-Detection
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
https://github.com/PennyLaneAI/pennylane
Better than classical? The subtle art of benchmarking quantum machine learning models
https://github.com/PermiJW/signSGD-with-Majority-Vote
signSGD with Majority Vote is Communication Efficient And Fault Tolerant
https://github.com/PolarisAlpha/darknet
https://github.com/PolyAI-LDN/polyai-models
Benchmarking Natural Language Understanding Services for building Conversational Agents
https://github.com/PolyAI-LDN/task-specific-datasets
Benchmarking Natural Language Understanding Services for building Conversational Agents
https://github.com/Popoooo/darknet_test
https://github.com/Precision-Medical-Imaging-Group/HOPE-Segmenter-Kids
https://github.com/ProhetTeam/QuantMmdetection
https://github.com/QCOIN-LAB/E2ECPE
End-to-end Emotion-Cause Pair Extraction via Learning to Link
https://github.com/RF5/CurveNet
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
https://github.com/RUCAIBox/RecBole
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
https://github.com/Rajwrita/Brain-Tumor-Auto-Segmentation
https://github.com/RangiLyu/mmdetection_test
https://github.com/RasaHQ/rasa
Benchmarking Natural Language Understanding Services for building Conversational Agents
https://github.com/RayeRen/RayeRen
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
https://github.com/RenXiangyuan/tf_bert
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/RenzKa/simlingo
Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.