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 1476–1500 of 10307 papers

Title	Date	Tasks	Status	Hype
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
What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs	Jun 4, 2019	Domain AdaptationTransfer Learning	CodeCode Available	1
Continual learning with hypernetworks	Jun 3, 2019	Continual LearningTransfer Learning	CodeCode Available	1
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions	May 24, 2019	Question AnsweringReading Comprehension	CodeCode Available	1
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems	May 21, 2019	Dialogue State TrackingMulti-domain Dialogue State Tracking	CodeCode Available	1
Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples	May 21, 2019	Drug DiscoveryTransfer Learning	CodeCode Available	1
ShapeGlot: Learning Language for Shape Differentiation	May 8, 2019	ObjectTransfer Learning	CodeCode Available	1
Improving and Understanding Variational Continual Learning	May 6, 2019	Continual LearningPermuted-MNIST	CodeCode Available	1
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems	May 2, 2019	Transfer Learning	CodeCode Available	1
Unsupervised Data Augmentation for Consistency Training	Apr 29, 2019	Data AugmentationImage Augmentation	CodeCode Available	1
Inductive Matrix Completion Based on Graph Neural Networks	Apr 26, 2019	Graph Neural NetworkMatrix Completion	CodeCode Available	1
Representation Similarity Analysis for Efficient Task taxonomy & Transfer Learning	Apr 26, 2019	SegmentationSemantic Segmentation	CodeCode Available	1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation	Apr 16, 2019	General ClassificationModel Selection	CodeCode Available	1
HAKE: Human Activity Knowledge Engine	Apr 13, 2019	Action DetectionHuman-Object Interaction Detection	CodeCode Available	1
Graphonomy: Universal Human Parsing via Graph Transfer Learning	Apr 9, 2019	Human ParsingTransfer Learning	CodeCode Available	1
Med3D: Transfer Learning for 3D Medical Image Analysis	Apr 1, 2019	3D Medical Imaging SegmentationLiver Segmentation	CodeCode Available	1
Learning to Transfer Examples for Partial Domain Adaptation	Mar 28, 2019	Domain AdaptationPartial Domain Adaptation	CodeCode Available	1
On Tiny Episodic Memories in Continual Learning	Feb 27, 2019	Class Incremental LearningContinual Learning	CodeCode Available	1
A Simple Baseline for Bayesian Uncertainty in Deep Learning	Feb 7, 2019	Bayesian InferenceDeep Learning	CodeCode Available	1
Parameter-Efficient Transfer Learning for NLP	Feb 2, 2019	Image ClassificationText Classification	CodeCode Available	1
Exploring Transfer Learning for Low Resource Emotional TTS	Jan 14, 2019	Deep LearningEmotional Speech Synthesis	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