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 8401–8425 of 10307 papers

TitleStatusHype
On Transfer Learning for a Fully Convolutional Deep Neural SIMO Receiver—0
Structural transfer learning of non-Gaussian DAG—0
On Hypothesis Transfer Learning of Functional Linear Models—0
On transfer learning of neural networks using bi-fidelity data for uncertainty propagation—0
On Transfer Learning of Traditional Frequency and Time Domain Features in Turning—0
On transfer learning using a MAC model variant—0
On Using Transfer Learning For Plant Disease Detection—0
Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning—0
OpenAg: Democratizing Agricultural Intelligence—0
OpenAVS: Training-Free Open-Vocabulary Audio Visual Segmentation with Foundational Models—0
A Feature Transfer Enabled Multi-Task Deep Learning Model on Medical Imaging—0
Structured Model Probing: Empowering Efficient Transfer Learning by Structured Regularization—0
A Feature Extraction based Model for Hate Speech Identification—0
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine—0
OpeNPDN: A Neural-network-based Framework for Power Delivery Network Synthesis—0
The Global Banking Standards QA Dataset (GBS-QA)—0
Open-Set Crowdsourcing using Multiple-Source Transfer Learning—0
Open Set Dandelion Network for IoT Intrusion Detection—0
Open-Set Fine-Grained Retrieval via Prompting Vision-Language Evaluator—0
A fast general thermal simulation model based on MultiBranch Physics-Informed deep operator neural network—0
Aerodynamic and structural airfoil shape optimisation via Transfer Learning-enhanced Deep Reinforcement Learning—0
A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition—0
Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules—0
Structured Variationally Auto-encoded Optimization—0
Optical Character Recognition (OCR) for Telugu: Database, Algorithm and Application—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