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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/moratodpg/imp_marl
공식 코드IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL
https://github.com/morgancthomas/molscore
공식 코드ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
https://github.com/morty-xu/tcc-bench
공식 코드TCC-Bench: Benchmarking the Traditional Chinese Culture Understanding Capabilities of MLLMs
https://github.com/mostafaalishahi/eICU_Benchmark
공식 코드Benchmarking machine learning models on multi-centre eICU critical care dataset
https://github.com/mostafaalishahi/eICU_Benchmark_updated
공식 코드Benchmarking machine learning models on multi-centre eICU critical care dataset
https://github.com/mostafajahanifar/histodomainbed
공식 코드Benchmarking Domain Generalization Algorithms in Computational Pathology
https://github.com/mostly-ai/mostlyai-qa
공식 코드Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework
https://github.com/motefly/LMA
공식 코드https://github.com/moyangkuo/audiomarkbench
공식 코드AudioMarkBench: Benchmarking Robustness of Audio Watermarking
https://github.com/mozerwang/loong
공식 코드Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA
https://github.com/mozhu621/SGT
공식 코드LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs
https://github.com/mozhu621/longgenbench
공식 코드LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs
https://github.com/mpc001/Lipreading_using_Temporal_Convolutional_Networks
공식 코드Towards Practical Lipreading with Distilled and Efficient Models
https://github.com/mproszewska/B-XAIC
공식 코드B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
https://github.com/mradaideh/neorl
공식 코드NEORL: NeuroEvolution Optimization with Reinforcement Learning
https://github.com/mrc-ide/referenced-TI
공식 코드https://github.com/mrcoee/automatic-label-error-detection
공식 코드https://github.com/mrgiovanni/abdomenatlas
공식 코드https://github.com/mrgiovanni/suprem
공식 코드https://github.com/mroughan/SurrealNumbers.jl
공식 코드Evolutionary Generation of Random Surreal Numbers for Benchmarking
https://github.com/mrwu-mac/EoID
공식 코드End-to-End Zero-Shot HOI Detection via Vision and Language Knowledge Distillation
https://github.com/msi-ru-cs/icse-seip2025-anomaly-detector-public
공식 코드Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset
https://github.com/msscao/acdepth
공식 코드Always Clear Depth: Robust Monocular Depth Estimation under Adverse Weather
https://github.com/mstrutov/desbordante
공식 코드Desbordante: from benchmarking suite to high-performance science-intensive data profiler (preprint)
https://github.com/msu-video-group/adversarial-defenses-for-iqa
공식 코드https://github.com/mu-cai/TemporalBench
공식 코드TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
https://github.com/muhammad-al-barbary/comoto
공식 코드CoMoTo: Unpaired Cross-Modal Lesion Distillation Improves Breast Lesion Detection in Tomosynthesis
https://github.com/mukherjeearnab/flsim
공식 코드FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
https://github.com/mukobi/welfare-diplomacy
공식 코드https://github.com/multi-iot/multiiot
공식 코드MultiIoT: Benchmarking Machine Learning for the Internet of Things
https://github.com/multi-modality-tracking/ckd
공식 코드Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation
https://github.com/mungg/OneRuler
공식 코드One ruler to measure them all: Benchmarking multilingual long-context language models
https://github.com/munozdjp/ihcv
공식 코드IHCV: Discovery of Hidden Time-Dependent Control Variables in Non-Linear Dynamical Systems
https://github.com/mustafaCoskunAgu/ScNP
공식 코드Graph Convolutional Networks Meet with High Dimensionality Reduction
https://github.com/muyang99/globalgeotree
공식 코드GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classification
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