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 2426–2450 of 10307 papers

TitleStatusHype
Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification—0
Proxy-informed Bayesian transfer learning with unknown sources—0
Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods—0
Leveraging Transfer Learning and Multiple Instance Learning for HER2 Automatic Scoring of H\&E Whole Slide ImagesCode0
A Mamba Foundation Model for Time Series Forecasting—0
Exploiting the Segment Anything Model (SAM) for Lung Segmentation in Chest X-ray Images—0
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning—0
Supervised Transfer Learning Framework for Fault Diagnosis in Wind Turbines—0
Personalized Continual EEG Decoding: Retaining and Transferring Knowledge—0
AM Flow: Adapters for Temporal Processing in Action Recognition—0
V-CAS: A Realtime Vehicle Anti Collision System Using Vision Transformer on Multi-Camera Streams—0
MultiBalance: Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System—0
Transfer Learning for Finetuning Large Language Models—0
Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior—0
Metric Learning for 3D Point Clouds Using Optimal Transport—0
LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering—0
Rethinking Inverse Reinforcement Learning: from Data Alignment to Task AlignmentCode0
BioNCERE: Non-Contrastive Enhancement For Relation Extraction In Biomedical Texts—0
Attention is All You Need to Optimize Wind Farm Operations and Maintenance—0
Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks—0
Sequential Order-Robust Mamba for Time Series ForecastingCode0
Nested ResNet: A Vision-Based Method for Detecting the Sensing Area of a Drop-in Gamma Probe—0
Don't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification—0
Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano—0
Self-Driving Car Racing: Application of Deep Reinforcement Learning—0
Show:102550
← PrevPage 98 of 413Next →
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