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Knowledge Distillation
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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/Vision-sejin/FDCNet
공식 코드https://github.com/Visual-AI/Semantic-Correspondence
공식 코드Semantic Correspondence: Unified Benchmarking and a Strong Baseline
https://github.com/Voice-Privacy-Challenge/Voice-Privacy-Challenge-2020
공식 코드https://github.com/WendongZh/SPL
공식 코드https://github.com/WenjieDu/PyPOTS
공식 코드https://github.com/Wenlve-Zhou/VLP-UDA
공식 코드Unsupervised Domain Adaption Harnessing Vision-Language Pre-training
https://github.com/WenxuanZhu1103/4D-Bench
공식 코드4D-Bench: Benchmarking Multi-modal Large Language Models for 4D Object Understanding
https://github.com/WesleyHsieh0806/TAO-Amodal
공식 코드https://github.com/Westlake-AI/openmixup
공식 코드GenURL: A General Framework for Unsupervised Representation Learning
https://github.com/WilsonWangTHU/POPLIN
공식 코드https://github.com/WonbinKweon/BD_WWW2021
공식 코드https://github.com/WorldMedQA/V
공식 코드https://github.com/XHWXD/DBSN
공식 코드https://github.com/XU-TIANYANG/LADCF
공식 코드https://github.com/XanaduAI/GradDFT
공식 코드Grad DFT: a software library for machine learning enhanced density functional theory
https://github.com/XanderJC/medkit-learn
공식 코드The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation
https://github.com/Xiang-Deng-DL/GFKD
공식 코드https://github.com/XiaoMi/MobileBench
공식 코드Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents
https://github.com/XiaojuanHu/AA_property_calculation
공식 코드Better force fields start with better data -- A data set of cation dipeptide interactions
https://github.com/Xiaoqiong-Liu/UAV-C
공식 코드Benchmarking the Robustness of UAV Tracking Against Common Corruptions
https://github.com/Xingyu-Lin/softgym
공식 코드SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation
https://github.com/XinshaoAmosWang/ProSelfLC-CVPR2021
공식 코드ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks
https://github.com/Xming-Z/RobustKD
공식 코드Robust Knowledge Distillation Based on Feature Variance Against Backdoored Teacher Model
https://github.com/Xnhyacinth/TAGI
공식 코드From Instance Training to Instruction Learning: Task Adapters Generation from Instructions
https://github.com/XueZ-phd/PR-FocusNet-PyTorch-ILSVRC2012
공식 코드FocusNet: Classifying Better by Focusing on Confusing Classes
https://github.com/Y-L-LIU/MGTBench-2.0
공식 코드On the Generalization Ability of Machine-Generated Text Detectors
https://github.com/YBIO/CPP
공식 코드https://github.com/YLab-Open/BRIDGE
공식 코드BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
https://github.com/YXSong000/UAD
공식 코드Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation
https://github.com/YangLIN1997/AMLNet-DSAA2023
공식 코드https://github.com/YangLiu9208/SAKDN
공식 코드Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition
https://github.com/YanghuiSong/DS_FusionNet
공식 코드https://github.com/YecanLee/2BeOETG
공식 코드Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework
https://github.com/YihanWang617/context_awareness
공식 코드On the Loss of Context-awareness in General Instruction Fine-tuning
https://github.com/Yinghao-Li/UncertaintyBenchmark
공식 코드MUBen: Benchmarking the Uncertainty of Molecular Representation Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.