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 926–950 of 10307 papers

TitleStatusHype
AraT5: Text-to-Text Transformers for Arabic Language GenerationCode1
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
Domain Adaptation of Thai Word Segmentation Models using Stacked EnsembleCode1
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
A Recent Survey of Heterogeneous Transfer LearningCode1
MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving PerceptionCode1
MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray ModelsCode1
Model-Based Reinforcement Learning with Isolated ImaginationsCode1
Deep Recurrent Neural Networks for ECG Signal DenoisingCode1
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?Code1
AReLU: Attention-based Rectified Linear UnitCode1
Affordance Transfer Learning for Human-Object Interaction DetectionCode1
DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep LearningCode1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised DataCode1
Models Genesis: Generic Autodidactic Models for 3D Medical Image AnalysisCode1
Deep Subdomain Adaptation Network for Image ClassificationCode1
WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management TasksCode1
Domain-Agnostic Molecular Generation with Chemical FeedbackCode1
An Empirical Study of Pre-trained Transformers for Arabic Information ExtractionCode1
Deep transfer operator learning for partial differential equations under conditional shiftCode1
Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac SegmentationCode1
MoVi: A large multi-purpose human motion and video datasetCode1
Deep Transferring QuantizationCode1
Efficient Few-Shot Object Detection via Knowledge InheritanceCode1
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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