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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 291–300 of 10307 papers

Title	Date	Tasks	Status	Hype
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty	May 4, 2023	Knowledge Distillationobject-detection	CodeCode Available	1
A Whisper transformer for audio captioning trained with synthetic captions and transfer learning	May 15, 2023	Audio captioningSpeech-to-Text	CodeCode Available	1
Benchmarking Detection Transfer Learning with Vision Transformers	Nov 22, 2021	Benchmarkingobject-detection	CodeCode Available	1
Automatic identification of segmentation errors for radiotherapy using geometric learning	Jun 27, 2022	Graph Neural NetworkSelf-Supervised Learning	CodeCode Available	1
Automatic Dialect Adaptation in Finnish and its Effect on Perceived Creativity	Sep 6, 2020	NMTTransfer Learning	CodeCode Available	1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning	Feb 13, 2023	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning	Sep 13, 2017	Cloud ComputingDeep Reinforcement Learning	CodeCode Available	1
AutoKE: An automatic knowledge embedding framework for scientific machine learning	May 11, 2022	BIG-bench Machine LearningNeural Architecture Search	CodeCode Available	1
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer Learning	Apr 25, 2020	Bayesian OptimizationTransfer Learning	CodeCode Available	1
Authorship Style Transfer with Policy Optimization	Mar 12, 2024	Style TransferTransfer Learning	CodeCode Available	1

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All datasets Office-Home 100 sleep nights of 8 caregivers BanglaLekha Isolated Dataset COCO70 KITTI Object Tracking Evaluation 2012 Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	APCLIP	Accuracy	84.2	—	Unverified
2	DFA-ENT	Accuracy	69.2	—	Unverified
3	DFA-SAFN	Accuracy	69.1	—	Unverified
4	EasyTL	Accuracy	63.3	—	Unverified
5	MEDA	Accuracy	60.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN	10-20% Mask PSNR	3.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Chatterjee, Dutta et al.[1]	Accuracy	96.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Co-Tuning	Accuracy	85.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Physical Access	EER	5.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	riadd.aucmedi	AUROC	0.95	—	Unverified