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 2331–2340 of 10307 papers

Title	Date	Tasks	Status	Score
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding	Oct 12, 2022	Multi-Task LearningTransfer Learning	CodeCode Available	5
FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations	May 17, 2020	Data AugmentationGeneral Classification	CodeCode Available	5
FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion	Mar 25, 2025	Depth EstimationMonocular Depth Estimation	CodeCode Available	5
Analysis and Prediction of NLP Models Via Task Embeddings	Dec 10, 2021	PredictionTransfer Learning	CodeCode Available	5
From Video Game to Real Robot: The Transfer between Action Spaces	May 2, 2019	Deep Reinforcement LearningReinforcement Learning	CodeCode Available	5
From English to Code-Switching: Transfer Learning with Strong Morphological Clues	Sep 11, 2019	Language IdentificationNamed Entity Recognition (NER)	CodeCode Available	5
From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal Images	Apr 8, 2019	Image SegmentationMedical Image Segmentation	CodeCode Available	5
FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning Techniques	Mar 13, 2020	ClassificationCPU	CodeCode Available	5
Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution	Oct 31, 2020	coreference-resolutionCoreference Resolution	CodeCode Available	5
From Colors to Classes: Emergence of Concepts in Vision Transformers	Mar 31, 2025	Domain AdaptationTransfer 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