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 1471–1480 of 10307 papers

Title	Date	Tasks	Status	Hype
KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks	Jul 29, 2019	Transfer Learning	CodeCode Available	1
WinoGrande: An Adversarial Winograd Schema Challenge at Scale	Jul 24, 2019	Common Sense ReasoningCoreference Resolution	CodeCode Available	1
Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems	Jul 12, 2019	Decision MakingLanguage Modeling	CodeCode Available	1
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning	Jul 1, 2019	Automatic Post-EditingDecoder	CodeCode Available	1
Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN	Jun 28, 2019	Image GenerationSegmentation	CodeCode Available	1
Evaluating Protein Transfer Learning with TAPE	Jun 19, 2019	BIG-bench Machine LearningRepresentation Learning	CodeCode Available	1
Transfer Learning for Causal Sentence Detection	Jun 18, 2019	RelationRelation Extraction	CodeCode Available	1
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes	Jun 18, 2019	Active LearningContinual Learning	CodeCode Available	1
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning	Jun 14, 2019	Automatic Post-EditingDecoder	CodeCode Available	1
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets	Jun 13, 2019	BenchmarkingDocument 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