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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy
LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization
Optimising Language Models for Downstream Tasks: A Post-Training Perspective
Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models
WordCon: Word-level Typography Control in Scene Text Rendering
Detecting Referring Expressions in Visually Grounded Dialogue with Autoregressive Language Models
Exploring Adapter Design Tradeoffs for Low Resource Music Generation
ARD-LoRA: Dynamic Rank Allocation for Parameter-Efficient Fine-Tuning of Foundation Models with Heterogeneous Adaptation Needs
Memba: Membrane-driven Parameter-Efficient Fine-Tuning for Mamba
Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters
GuiLoMo: Allocating Expert Number and Rank for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors
Prefix-Tuning+: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention
Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning
FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning
FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
AR-RAG: Autoregressive Retrieval Augmentation for Image Generation
Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
Dynamic Mixture of Progressive Parameter-Efficient Expert Library for Lifelong Robot Learning
InstantFT: An FPGA-Based Runtime Subsecond Fine-tuning of CNN Models
Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-Order
Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning
Matching Markets Meet LLMs: Algorithmic Reasoning with Ranked Preferences
WeightLoRA: Keep Only Necessary Adapters
Parameter Efficient Fine Tuning Llama 3.1 for Answering Arabic Legal Questions: A Case Study on Jordanian Laws
Uni-LoRA: One Vector is All You Need
LoRA as a Flexible Framework for Securing Large Vision Systems
Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection
On Fairness of Task Arithmetic: The Role of Task Vectors
CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning
MAP: Revisiting Weight Decomposition for Low-Rank Adaptation
Weight Spectra Induced Efficient Model Adaptation
Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning Dynamics
DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers
SC-LoRA: Balancing Efficient Fine-tuning and Knowledge Preservation via Subspace-Constrained LoRA
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.