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 3231–3240 of 10307 papers

Title	Date	Tasks	Status	Score
Exclusive Supermask Subnetwork Training for Continual Learning	Oct 18, 2022	Continual LearningText Classification	CodeCode Available	5
Explicit Inductive Bias for Transfer Learning with Convolutional Networks	Feb 5, 2018	Inductive BiasTransfer Learning	CodeCode Available	5
DS@GT at CheckThat! 2025: Detecting Subjectivity via Transfer-Learning and Corrective Data Augmentation	Jul 8, 2025	ARCData Augmentation	CodeCode Available	5
Evaluation and Comparison of Deep Learning Methods for Pavement Crack Identification with Visual Images	Dec 20, 2021	Contact DetectionDecoder	CodeCode Available	5
Evaluation of deep neural networks for traffic sign detection systems	Nov 17, 2018	Autonomous Drivingobject-detection	CodeCode Available	5
Evaluating the Values of Sources in Transfer Learning	Apr 26, 2021	Cross-Lingual POS TaggingTransfer Learning	CodeCode Available	5
A Survey on Causal Representation Learning and Future Work for Medical Image Analysis	Oct 28, 2022	Causal InferenceMedical Image Analysis	CodeCode Available	5
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain	May 12, 2022	Ensemble LearningMachine Reading Comprehension	CodeCode Available	5
Conversational AI for Positive-sum Retailing under Falsehood Control	May 1, 2022	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	5
Evaluating deep transfer learning for whole-brain cognitive decoding	Nov 1, 2021	Transfer Learning	CodeCode Available	5

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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