Papers with Code 아카이브

Transfer Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 4
평가표: 6
보존된 평가 기록: 10

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

<span style="color:grey; opacity: 0.6">( Image credit: Subodh Malgonde )</span>

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Transfer Learning

논문

A Computational Model of Teacher-Student Co-Adaptation

Transferring Visual Explainability from Self-Explaining to Prediction-Only Vision Transformers via Task Arithmetic

Breccia and basalt classification of thin sections of Apollo rocks with deep learning

RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction

Disentangling coincident cell events using deep transfer learning and compressive sensing

SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation

Best Practices for Large-Scale, Pixel-Wise Crop Mapping and Transfer Learning Workflows

Robust-Multi-Task Gradient Boosting

Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift

The Bayesian Approach to Continual Learning: An Overview

SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation

PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning

Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol

A Survey on Prompt Tuning

DS@GT at CheckThat! 2025: Detecting Subjectivity via Transfer-Learning and Corrective Data Augmentation

Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving

GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation

Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures

High-Order Deep Meta-Learning with Category-Theoretic Interpretation

Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains

Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature Alignment

FedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model

Distilling Normalizing Flows

DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic

Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation

Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-Supervision, Mixture of Experts and Multi-Task Integration

FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment

Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach

Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning

Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Laser Powder Bed Fusion

Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher

AnchorDP3: 3D Affordance Guided Sparse Diffusion Policy for Robotic Manipulation

From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data

These are Not All the Features You are Looking For: A Fundamental Bottleneck In Supervised Pretraining

Leveraging neural network interatomic potentials for a foundation model of chemistry

Generalizing Vision-Language Models to Novel Domains: A Comprehensive Survey