https://github.com/dianagrigore/wecolora
공식 코드Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers
Papers with Code 아카이브
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/dice-group/CostBased-FedEval
공식 코드An Empirical Evaluation of Cost-based Federated SPARQL Query Processing Engines
https://github.com/dice-group/evolearner
공식 코드EvoLearner: Learning Description Logics with Evolutionary Algorithms
https://github.com/dichao-liu/lightweight_distracted_driver_recognition_with_distillation-based_nas_and_knowledge_transfer
공식 코드https://github.com/dido1998/CausalMBRL
공식 코드Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
https://github.com/diegoalvareze/qkt_benchmarking
공식 코드Benchmarking quantum machine learning kernel training for classification tasks
https://github.com/diehlpkpapers/ai-journal-paper
공식 코드https://github.com/digital-dermatology/cleanpatrick
공식 코드https://github.com/diliadis/deepmtp
공식 코드https://github.com/dilis-lab/simbaml_examples
공식 코드SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented Data
https://github.com/dilyabareeva/quanda
공식 코드Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyond
https://github.com/diponkor-bala/autism-spectrum-disorder
공식 코드An Evaluation of Machine Learning Approaches for Early Diagnosis of Autism Spectrum Disorder
https://github.com/disl-lab/finesure-acl24
공식 코드https://github.com/distill-generalization-group/distill-generalization
공식 코드Improve Cross-Architecture Generalization on Dataset Distillation
https://github.com/distributionnetworkstudelft/ev2gym
공식 코드EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking
https://github.com/div290/capeen
공식 코드CAPEEN: Image Captioning with Early Exits and Knowledge Distillation
https://github.com/div290/dadee
공식 코드https://github.com/divelab/DIG
공식 코드DIG: A Turnkey Library for Diving into Graph Deep Learning Research
https://github.com/dk-liang/uniseg3d
공식 코드https://github.com/dkgupta90/re-siamnet
공식 코드https://github.com/dkozlov/awesome-knowledge-distillation
공식 코드https://github.com/dl-wg/gcn-lstm-wildfire
공식 코드Explainable Global Wildfire Prediction Models using Graph Neural Networks
https://github.com/dlr-mi/pim
공식 코드https://github.com/dmadras/nooch
공식 코드Identifying and Benchmarking Natural Out-of-Context Prediction Problems
https://github.com/dmah10/EvalxNLP
공식 코드EvalxNLP: A Framework for Benchmarking Post-Hoc Explainability Methods on NLP Models
https://github.com/docailab/xrag
공식 코드https://github.com/docu-mint/documint
공식 코드DocuMint: Docstring Generation for Python using Small Language Models
https://github.com/dominickrei/egoexo4adl
공식 코드https://github.com/dongchen06/scooterdet
공식 코드Performance Evaluation of Real-Time Object Detection for Electric Scooters
https://github.com/dongjing3309/minisam
공식 코드miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework
https://github.com/dongyh20/octopus
공식 코드Octopus: Embodied Vision-Language Programmer from Environmental Feedback
https://github.com/dontfollowmeimcrazy/vot-kd-rl
공식 코드https://github.com/doviledo/transferability-in-medical-imaging
공식 코드https://github.com/dphuongn/FedKEM
공식 코드Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion
https://github.com/dptech-corp/Uni-Mol
공식 코드Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+
https://github.com/dptech-corp/nag2g
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.