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 1161–1170 of 10307 papers

Title	Date	Tasks	Status	Hype
Parameter-Efficient Transfer Learning with Diff Pruning	Dec 14, 2020	Transfer Learning	CodeCode Available	1
Sim-to-real reinforcement learning applied to end-to-end vehicle control	Dec 14, 2020	Collision Avoidancereinforcement-learning	CodeCode Available	1
Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks	Dec 13, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning	Dec 11, 2020	Electron Microscopy Image SegmentationSegmentation	CodeCode Available	1
Leveraging Transfer Learning for Reliable Intelligence Identification on Vietnamese SNSs (ReINTEL)	Dec 10, 2020	Reliable Intelligence IdentificationTransfer Learning	CodeCode Available	1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification	Dec 9, 2020	Anomaly DetectionAtrial Fibrillation Detection	CodeCode Available	1
Progressive Network Grafting for Few-Shot Knowledge Distillation	Dec 9, 2020	Knowledge DistillationModel Compression	CodeCode Available	1
Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning	Dec 7, 2020	ClassificationGeneral Classification	CodeCode Available	1
Cross-Layer Distillation with Semantic Calibration	Dec 6, 2020	Knowledge DistillationTransfer Learning	CodeCode Available	1
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking	Dec 1, 2020	Pose TrackingTransfer 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