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 1501–1510 of 10307 papers

Title	Date	Tasks	Status	Hype
High Quality Monocular Depth Estimation via Transfer Learning	Dec 31, 2018	Action ClassificationDecoder	CodeCode Available	1
Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions	Dec 10, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Meta-Transfer Learning for Few-Shot Learning	Dec 6, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Bag of Tricks for Image Classification with Convolutional Neural Networks	Dec 4, 2018	Domain GeneralizationGeneral Classification	CodeCode Available	1
Transferring Knowledge across Learning Processes	Dec 3, 2018	Meta-LearningReinforcement Learning	CodeCode Available	1
Transfer learning for time series classification	Nov 5, 2018	ClassificationDynamic Time Warping	CodeCode Available	1
Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary	Nov 3, 2018	Machine TranslationNMT	CodeCode Available	1
Contour Knowledge Transfer for Salient Object Detection	Sep 1, 2018	Contour DetectionObject	CodeCode Available	1
Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach	Aug 8, 2018	EEGElectroencephalogram (EEG)	CodeCode Available	1
How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs	Aug 1, 2018	Multi-Task LearningTransfer Learning	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