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 6526–6550 of 10307 papers

TitleStatusHype
Semi-supervised Learning of Naive Bayes Classifier with feature constraints—0
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation—0
Integrating Deep Features for Material Recognition—0
An Explainable Vision Transformer with Transfer Learning Combined with Support Vector Machine Based Efficient Drought Stress Identification—0
Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline—0
Intelligent Chemical Purification Technique Based on Machine Learning—0
Intelligent Incident Hypertension Prediction in Obstructive Sleep Apnea—0
Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN—0
Intelligent multiscale simulation based on process-guided composite database—0
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities—0
Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks—0
Interactive dimensionality reduction using similarity projections—0
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models—0
InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions—0
Inter- and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition—0
Inter-Cell Network Slicing With Transfer Learning Empowered Multi-Agent Deep Reinforcement Learning—0
Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection—0
Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?—0
Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?—0
Interpolation-Free Deep Learning for Meteorological Downscaling on Unaligned Grids Across Multiple Domains with Application to Wind Power—0
Semi-Supervised Lifelong Language Learning—0
Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images—0
On Deep Domain Adaptation: Some Theoretical Understandings—0
Interpretable Deep Learning applied to Plant Stress Phenotyping—0
A Physics-preserved Transfer Learning Method for Differential Equations—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