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 711–720 of 10307 papers

Title	Date	Tasks	Status	Hype
Factorizing Knowledge in Neural Networks	Jul 4, 2022	DisentanglementTransfer Learning	CodeCode Available	1
CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question Answering	Jul 1, 2022	Language ModelingLanguage Modelling	CodeCode Available	1
No Reason for No Supervision: Improved Generalization in Supervised Models	Jun 30, 2022	Data AugmentationSelf-Supervised Learning	CodeCode Available	1
Transfer Learning with Deep Tabular Models	Jun 30, 2022	Medical DiagnosisTransfer Learning	CodeCode Available	1
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning	Jun 27, 2022	Action ClassificationAction Recognition	CodeCode Available	1
Automatic identification of segmentation errors for radiotherapy using geometric learning	Jun 27, 2022	Graph Neural NetworkSelf-Supervised Learning	CodeCode Available	1
SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data	Jun 21, 2022	Face RecognitionGenerative Adversarial Network	CodeCode Available	1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification	Jun 20, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks	Jun 18, 2022	Continual LearningTransfer Learning	CodeCode Available	1
Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning	Jun 18, 2022	AnatomyGaze Estimation	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