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 4171–4180 of 10307 papers

Title	Date	Tasks	Status	Hype
Transfer-learning for video classification: Video Swin Transformer on multiple domains	Oct 18, 2022	Transfer LearningVideo Classification	—Unverified	0
Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection	Oct 18, 2022	Change DetectionHyperparameter Optimization	—Unverified	0
Conditional Neural Processes for Molecules	Oct 17, 2022	Bayesian OptimizationBenchmarking	—Unverified	0
Review Learning: Alleviating Catastrophic Forgetting with Generative Replay without Generator	Oct 17, 2022	Binary ClassificationContinual Learning	—Unverified	0
6th Place Solution to Google Universal Image Embedding	Oct 17, 2022	Transfer Learning	—Unverified	0
A Transfer Learning Based Approach for Classification of COVID-19 and Pneumonia in CT Scan Imaging	Oct 17, 2022	Transfer Learning	—Unverified	0
Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores	Oct 16, 2022	Transfer Learning	CodeCode Available	0
Aplicación de redes neuronales convolucionales profundas al diagnóstico asistido de la enfermedad de Alzheimer	Oct 15, 2022	Data AugmentationTransfer Learning	CodeCode Available	0
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties	Oct 14, 2022	Drug DiscoveryTransfer Learning	CodeCode Available	0
Is synthetic data from generative models ready for image recognition?	Oct 14, 2022	Image GenerationText to Image Generation	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