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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5026–5050 of 10307 papers

TitleStatusHype
Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better—0
Knowledge Management for Automobile Failure Analysis Using Graph RAG—0
Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science—0
Knowledge Squeezed Adversarial Network Compression—0
Knowledge Transfer Across Modalities with Natural Language Supervision—0
Knowledge Transfer across Multiple Principal Component Analysis Studies—0
Knowledge Transfer Between Artificial Intelligence Systems—0
Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning—0
Knowledge Transfer between Buildings for Seismic Damage Diagnosis through Adversarial Learning—0
Knowledge Transfer between Datasets for Learning-based Tissue Microstructure Estimation—0
Knowledge transfer between speakers for personalised dialogue management—0
Knowledge Transfer between Structured and Unstructured Sources for Complex Question Answering—0
Knowledge Transfer by Discriminative Pre-training for Academic Performance Prediction—0
Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review—0
Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives—0
Knowledge Transfer for Efficient On-device False Trigger Mitigation—0
Knowledge Transfer for Few-shot Segmentation of Novel White Matter Tracts—0
Knowledge Transfer for Pseudo-code Generation from Low Resource Programming Language—0
Knowledge Transfer for Scene-specific Motion Prediction—0
Knowledge transfer for surgical activity prediction—0
Knowledge Transfer from Answer Ranking to Answer Generation—0
Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs—0
Knowledge Transfer from Large-scale Pretrained Language Models to End-to-end Speech Recognizers—0
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning—0
Knowledge transfer in deep block-modular neural networks—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