https://github.com/AI-Application-and-Integration-Lab/Accept
공식 코드ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning
Papers with Code 아카이브
parameter-efficient fine-tuning
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Parameter-Efficient Fine-Tuning (PEFT) is a technique used to adapt pre-trained models to new tasks with minimal changes to the model's parameters. This approach is particularly useful in scenarios where computational resources are limited or when it is desirable to maintain the original model's performance on the initial task.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AI4Bharat/adapter-efficiency
공식 코드https://github.com/Amirresm/empirical_peft_suite
공식 코드https://github.com/AndrewPBerg/UAV_Classification
공식 코드4,500 Seconds: Small Data Training Approaches for Deep UAV Audio Classification
https://github.com/Aradhye2002/selective-peft-toolkit
공식 코드Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language Models
https://github.com/ArtmeScienceLab/FonTS
공식 코드https://github.com/Azadeh297/BiGTex
공식 코드Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex
https://github.com/BBYL9413/TDS-CLIP
공식 코드TDS-CLIP: Temporal Difference Side Network for Image-to-Video Transfer Learning
https://github.com/BanafshehKarimian/ModalityPairing
공식 코드CLIP-IT: CLIP-based Pairing for Histology Images Classification
https://github.com/BenediktAlkin/vtab1k-pytorch
공식 코드Parameter Efficient Fine-tuning via Explained Variance Adaptation
https://github.com/CandleLabAI/MotionAura-ICLR-2025
공식 코드MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion
https://github.com/Chongjie-Si/Subspace-Tuning
공식 코드See Further for Parameter Efficient Fine-tuning by Standing on the Shoulders of Decomposition
https://github.com/ChrisIsKing/Parameter-Efficient-Personalization
공식 코드PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization
https://github.com/ChunyuLiu188/SpectrumFM
공식 코드SpectrumFM: A Foundation Model for Intelligent Spectrum Management
https://github.com/Deagogishvili/chapter-multi-task
공식 코드PatchProt: Hydrophobic patch prediction using protein foundation models
https://github.com/EdinburghClinicalNLP/semeval_nli4ct
공식 코드Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model unless you have access to GPT-4
https://github.com/EvenJoker/Point-PEFT
공식 코드Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models
https://github.com/FairMedFM/FairMedFM
공식 코드FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models
https://github.com/FederatedAI/FATE-LLM
공식 코드FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models
https://github.com/FightingFighting/GPS
공식 코드Gradient-based Parameter Selection for Efficient Fine-Tuning
https://github.com/FineDiffusion/FineDiffusion
공식 코드FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 Classes
https://github.com/Gunale0926/SORSA
공식 코드https://github.com/HarryLui98/DMPEL
공식 코드Dynamic Mixture of Progressive Parameter-Efficient Expert Library for Lifelong Robot Learning
https://github.com/HotanLee/DeFT
공식 코드https://github.com/IBM/AutoVP
공식 코드AutoVP: An Automated Visual Prompting Framework and Benchmark
https://github.com/IBM/SpaRTA
공식 코드Sparsity May Be All You Need: Sparse Random Parameter Adaptation
https://github.com/JCruan519/GIST
공식 코드GIST: Improving Parameter Efficient Fine Tuning via Knowledge Interaction
https://github.com/Jiacheng-Zhu-AIML/AsymmetryLoRA
공식 코드https://github.com/JingXuTHU/Random-Masking-Finds-Winning-Tickets-for-Parameter-Efficient-Fine-tuning
공식 코드Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning
https://github.com/JudgingH/PEGO
공식 코드https://github.com/KU-CVLAB/MoA
공식 코드Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters
https://github.com/KlozeWang/LoSiA
공식 코드LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization
https://github.com/Kowsher/Propulsion
공식 코드https://github.com/Kowsher/RoCoFT
공식 코드RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates
https://github.com/MediaBrain-SJTU/RD
공식 코드https://github.com/NVIDIA/NeMo
공식 코드Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.