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 761–770 of 10307 papers

Title	Date	Tasks	Status	Hype
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training	Apr 24, 2022	Transfer Learning	CodeCode Available	1
Smart App Attack: Hacking Deep Learning Models in Android Apps	Apr 23, 2022	Adversarial AttackBinary Classification	CodeCode Available	1
A benchmark dataset for deep learning-based airplane detection: HRPlanes	Apr 22, 2022	Deep Learningobject-detection	CodeCode Available	1
Progressive Training of A Two-Stage Framework for Video Restoration	Apr 21, 2022	Super-ResolutionTransfer Learning	CodeCode Available	1
Deep transfer operator learning for partial differential equations under conditional shift	Apr 20, 2022	Domain AdaptationOperator learning	CodeCode Available	1
Self-supervised Learning for Sonar Image Classification	Apr 20, 2022	ClassificationDenoising	CodeCode Available	1
Safe Self-Refinement for Transformer-based Domain Adaptation	Apr 16, 2022	Domain AdaptationTransfer Learning	CodeCode Available	1
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference	Apr 15, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
DeiT III: Revenge of the ViT	Apr 14, 2022	Data Augmentationimage-classification	CodeCode Available	1
Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets and Contrastive Molecular Explanations	Apr 13, 2022	Transfer 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