https://github.com/DeepLabCut/DeepLabCut
공식 코드Pretraining boosts out-of-domain robustness for pose estimation
Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/DefangChen/OKDDip-AAAI2020
공식 코드https://github.com/DefangChen/SemCKD
공식 코드https://github.com/DefangChen/SimKD
공식 코드https://github.com/DengPingFan/CoEGNet
공식 코드https://github.com/DerrickXuNu/OpenCOOD
공식 코드https://github.com/Diamondfan/SPAPL_KidsASR
공식 코드Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation Models
https://github.com/Dingkun0817/SDDA
공식 코드Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
https://github.com/DixonCider/TCRI-Benchmark
공식 코드https://github.com/DoctorKey/LSHFM.detection
공식 코드https://github.com/DoctorKey/LSHFM.multiclassification
공식 코드https://github.com/DoctorKey/LSHFM.singleclassification
공식 코드https://github.com/Don767/segdet_mlcr2024
공식 코드Replication Study and Benchmarking of Real-Time Object Detection Models
https://github.com/Dongqi-Fan/ConStyle_v2
공식 코드ConStyle v2: A Strong Prompter for All-in-One Image Restoration
https://github.com/DrSleep/multi-task-refinenet
공식 코드Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
https://github.com/Dreamyao516/DialogueLLM
공식 코드https://github.com/DrenFazlija/AccessDeniedInc
공식 코드ACCESS DENIED INC: The First Benchmark Environment for Sensitivity Awareness
https://github.com/Duplums/bhb10k-dl-benchmark
공식 코드https://github.com/Dynamics-of-Neural-Systems-Lab/MARBLE
공식 코드Interpretable statistical representations of neural population dynamics and geometry
https://github.com/EPFL-VILAB/3DCommonCorruptions
공식 코드https://github.com/ETH-DISCO/Benchmarking-PEs
공식 코드Benchmarking Positional Encodings for GNNs and Graph Transformers
https://github.com/EagleMIT/EMKD
공식 코드Efficient Medical Image Segmentation Based on Knowledge Distillation
https://github.com/Eaphan/Robust3DOD
공식 코드https://github.com/Earth-Intelligence-Lab/vleo-bench
공식 코드Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data
https://github.com/EasyFL-AI/EasyFL/tree/master/applications/fedreid
공식 코드Performance Optimization for Federated Person Re-identification via Benchmark Analysis
https://github.com/EdenBelouadah/class-incremental-learning
공식 코드https://github.com/EdisonLeeeee/GraphGallery
공식 코드https://github.com/EduardoTrevino/fail-talms
공식 코드https://github.com/Efstathia-Soufleri/PKD
공식 코드Advancing Compressed Video Action Recognition through Progressive Knowledge Distillation
https://github.com/Egg-Hu/BiDf-MKD
공식 코드Learning to Learn from APIs: Black-Box Data-Free Meta-Learning
https://github.com/Emma926/paradnn
공식 코드https://github.com/EpistasisLab/ReBATE
공식 코드Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining
https://github.com/EpistasisLab/digen
공식 코드Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers
https://github.com/EpistasisLab/penn-ml-benchmarks
공식 코드PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison
https://github.com/EpistasisLab/pmlb
공식 코드PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
https://github.com/EpistasisLab/pmlb-manuscript
공식 코드PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.