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 9701–9725 of 10307 papers

TitleStatusHype
Learning More Universal Representations for Transfer-LearningCode0
Learning Physical Concepts in Cyber-Physical Systems: A Case StudyCode0
Learning Robust Precipitation Forecaster by Temporal Frame InterpolationCode0
Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus ImagesCode0
Learning Single-View 3D Reconstruction with Limited Pose SupervisionCode0
Learning Sound Events From Webly Labeled DataCode0
Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence FunctionCode0
Learning Text Representations for 500K Classification Tasks on Named Entity DisambiguationCode0
Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational ObjectiveCode0
Learning Time-Sensitive Strategies in Space FortressCode0
Learning to cluster in order to transfer across domains and tasksCode0
Learning to Generalize Compositionally by Transferring Across Semantic Parsing TasksCode0
Learning to Plan with Natural LanguageCode0
Learning to Prompt Knowledge Transfer for Open-World Continual LearningCode0
Learning to Rank Query Graphs for Complex Question Answering over Knowledge GraphsCode0
Learning to select data for transfer learning with Bayesian OptimizationCode0
Learning Unbiased Transferability for Domain Adaptation by Uncertainty ModelingCode0
Learning unbiased zero-shot semantic segmentation networks via transductive transferCode0
Semi-supervised Vector-valued Learning: Improved Bounds and AlgorithmsCode0
Learning What and Where to TransferCode0
Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random ForestsCode0
LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data GenerationCode0
Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer LearningCode0
Lessons from Natural Language Inference in the Clinical DomainCode0
Levenshtein Training for Word-level Quality EstimationCode0
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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