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 1371–1380 of 10307 papers

Title	Date	Tasks	Status	Hype
Learning context-aware structural representations to predict antigen and antibody binding interfaces	Apr 22, 2020	Antibody-antigen binding predictionTransfer Learning	CodeCode Available	1
Chip Placement with Deep Reinforcement Learning	Apr 22, 2020	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1
Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning	Apr 20, 2020	SentenceTransfer Learning	CodeCode Available	1
A Chinese Corpus for Fine-grained Entity Typing	Apr 19, 2020	Cross-Lingual TransferEntity Typing	CodeCode Available	1
Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting	Apr 17, 2020	Graph Neural NetworkTime Series Analysis	CodeCode Available	1
Bridging Anaphora Resolution as Question Answering	Apr 16, 2020	Bridging Anaphora ResolutionQuestion Answering	CodeCode Available	1
Transfer learning in large-scale ocean bottom seismic wavefield reconstruction	Apr 15, 2020	Transfer Learning	CodeCode Available	1
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer	Apr 14, 2020	Graph LearningGraph structure learning	CodeCode Available	1
Melanoma Detection using Adversarial Training and Deep Transfer Learning	Apr 14, 2020	General ClassificationImage Generation	CodeCode Available	1
Melanoma Detection using Adversarial Training and Deep Transfer Learning	Apr 14, 2020	Conditional Image GenerationGeneral Classification	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