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 9251–9275 of 10307 papers

TitleStatusHype
DSG-KD: Knowledge Distillation from Domain-Specific to General Language ModelsCode0
DS@GT at CheckThat! 2025: Detecting Subjectivity via Transfer-Learning and Corrective Data AugmentationCode0
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
Dynamic Bayesian Learning for Spatiotemporal Mechanistic ModelsCode0
Dynamic Guidance Adversarial Distillation with Enhanced Teacher KnowledgeCode0
Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud SegmentationCode0
Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural NetworksCode0
EvoluNet: Advancing Dynamic Non-IID Transfer Learning on GraphsCode0
E2Net: Resource-Efficient Continual Learning with Elastic Expansion NetworkCode0
E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited DataCode0
Early Life Cycle Software Defect Prediction. Why? How?Code0
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared VocabulariesCode0
Effective Cross-Task Transfer Learning for Explainable Natural Language Inference with T5Code0
Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity RecognitionCode0
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality DetectionCode0
Effects of the Nonlinearity in Activation Functions on the Performance of Deep Learning ModelsCode0
Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival dataCode0
Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal FeaturesCode0
EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware PseudocoloringCode0
Efficient Computation Sharing for Multi-Task Visual Scene UnderstandingCode0
Efficient Deep Learning Architectures for Fast Identification of Bacterial Strains in Resource-Constrained DevicesCode0
Efficient Deep Learning Methods for Identification of Defective Casting ProductsCode0
Efficient Deep Reinforcement Learning via Adaptive Policy TransferCode0
Efficient Entity Candidate Generation for Low-Resource LanguagesCode0
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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