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 3161–3170 of 10307 papers

Title	Date	Tasks	Status	Hype
Generating Synthetic Datasets by Interpolating along Generalized Geodesics	Jun 12, 2023	Transfer Learning	—Unverified	0
Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning	Jun 12, 2023	Neural Architecture SearchTransfer Learning	—Unverified	0
An information-Theoretic Approach to Semi-supervised Transfer Learning	Jun 11, 2023	Transfer Learning	—Unverified	0
VBSF-TLD: Validation-Based Approach for Soft Computing-Inspired Transfer Learning in Drone Detection	Jun 11, 2023	Transfer Learning	—Unverified	0
Enhancing Low Resource NER Using Assisting Language And Transfer Learning	Jun 10, 2023	named-entity-recognitionNamed Entity Recognition	—Unverified	0
PoET: A generative model of protein families as sequences-of-sequences	Jun 9, 2023	Language ModellingMultiple Sequence Alignment	CodeCode Available	1
SARN: Structurally-Aware Recurrent Network for Spatio-Temporal Disaggregation	Jun 9, 2023	Super-ResolutionTransfer Learning	CodeCode Available	0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates	Jun 9, 2023	Neural Architecture SearchNeural Network Compression	—Unverified	0
Emotion Detection from EEG using Transfer Learning	Jun 9, 2023	Data AugmentationEEG	—Unverified	0
Understanding the Benefits of Image Augmentations	Jun 9, 2023	Transfer Learning	—Unverified	0

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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