SOTAVerified

Transfer Learning

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.

( Image credit: Subodh Malgonde )

Papers

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Showing 376–400 of 10307 papers

TitleStatusHype
Transfer learning from first-principles calculations to experiments with chemistry-informed domain transformation—0
Repurposing 2D Diffusion Models with Gaussian Atlas for 3D Generation—0
UniCrossAdapter: Multimodal Adaptation of CLIP for Radiology Report GenerationCode0
Sample-Efficient Bayesian Transfer Learning for Online Machine Parameter Optimization—0
Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach—0
PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks—0
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions—0
Boosting HDR Image Reconstruction via Semantic Knowledge Transfer—0
A Novel Channel Boosted Residual CNN-Transformer with Regional-Boundary Learning for Breast Cancer Detection—0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis—0
Entity-aware Cross-lingual Claim Detection for Automated Fact-checkingCode0
Sequential learning based PINNs to overcome temporal domain complexities in unsteady flow past flapping wings—0
ELTEX: A Framework for Domain-Driven Synthetic Data GenerationCode0
Cross-Environment Transfer Learning for Location-Aided Beam Prediction in 5G and Beyond Millimeter-Wave Networks—0
Multi-task Learning for Identification of Porcelain in Song and Yuan Dynasties—0
SCJD: Sparse Correlation and Joint Distillation for Efficient 3D Human Pose EstimationCode0
LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data GenerationCode0
Island-Based Evolutionary Computation with Diverse Surrogates and Adaptive Knowledge Transfer for High-Dimensional Data-Driven OptimizationCode0
An Optimization Framework for Differentially Private Sparse Fine-Tuning—0
Edit Transfer: Learning Image Editing via Vision In-Context Relations—0
Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation—0
Robot Policy Transfer with Online Demonstrations: An Active Reinforcement Learning Approach—0
MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions DatasetCode1
Lifelong Reinforcement Learning with Similarity-Driven Weighting by Large Models—0
TuneNSearch: a hybrid transfer learning and local search approach for solving vehicle routing problems—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified