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 601–625 of 10307 papers

TitleStatusHype
Deep Image Harmonization by Bridging the Reality GapCode1
DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation TrainerCode1
APTv2: Benchmarking Animal Pose Estimation and Tracking with a Large-scale Dataset and BeyondCode1
Assemble Foundation Models for Automatic Code SummarizationCode1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
Association Graph Learning for Multi-Task Classification with Category ShiftsCode1
A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet DatasetCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samplesCode1
AraT5: Text-to-Text Transformers for Arabic Language GenerationCode1
CtrlFormer: Learning Transferable State Representation for Visual Control via TransformerCode1
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language ProcessingCode1
A Recent Survey of Heterogeneous Transfer LearningCode1
Alice: Proactive Learning with Teacher's Demonstrations for Weak-to-Strong GeneralizationCode1
A Survey on Recent Approaches for Natural Language Processing in Low-Resource ScenariosCode1
A Text Classification-Based Approach for Evaluating and Enhancing the Machine Interpretability of Building CodesCode1
MTTrans: Cross-Domain Object Detection with Mean-Teacher TransformerCode1
A Systematic Benchmarking Analysis of Transfer Learning for Medical Image AnalysisCode1
A systematic approach to deep learning-based nodule detection in chest radiographsCode1
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative StudyCode1
Deep transfer operator learning for partial differential equations under conditional shiftCode1
Cross-Domain Structure Preserving Projection for Heterogeneous Domain AdaptationCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Algorithmic encoding of protected characteristics in image-based models for disease detectionCode1
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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