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SotA/Methodology/Transfer Learning

Transfer Learning

24 benchmarks10307 papers

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.

<span style="color:grey; opacity: 0.6">( Image credit: Subodh Malgonde )</span>

Benchmarks

Transfer Learning on MML

Average (%)

Transfer Learning on BBH-nlp

Average (%)

Transfer Learning on MGSM

Average (%)

Transfer Learning on BBH-alg

Average (%)

Transfer Learning on Office-Home

Accuracy

Transfer Learning on QM9

∆m%

Transfer Learning on Cityscapes test

mIoURMSE

Transfer Learning on NYUv2

Mean IoU

Transfer Learning on OMNIGLOT

Average Accuracy

Transfer Learning on 100 sleep nights of 8 caregivers

10-20% Mask PSNR

Transfer Learning on BanglaLekha Isolated Dataset

Accuracy

Transfer Learning on COCO70

Accuracy

Transfer Learning on CelebA

Error

Transfer Learning on KITTI Object Tracking Evaluation 2012

EER

Transfer Learning on MMLU (5-Shot)

MMLU (5-shot)

Transfer Learning on Retinal Fundus MultiDisease Image Dataset (RFMiD)

AUROC

Transfer Learning on UDE-DomainNet

1:1 Accuracy

Transfer Learning on UDE-Office-Home

1:1 Accuracy

Transfer Learning on UTKFace

delta_m

Transfer Learning on wireframe dataset

FHsAP10sAP15

Transfer Learning on ChestX-ray14

delta_m