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 1481–1490 of 10307 papers

Title	Date	Tasks	Status	Hype	Score
Callee: Recovering Call Graphs for Binaries with Transfer and Contrastive Learning	Nov 2, 2021	Contrastive LearningQuestion Answering	CodeCode Available	1	5
Efficient parametrization of multi-domain deep neural networks	Mar 27, 2018	Continual LearningTransfer Learning	CodeCode Available	1	5
Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones	Mar 16, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available	1	5
Efficient transfer learning for NLP with ELECTRA	Apr 6, 2021	Transfer Learning	CodeCode Available	1	5
Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation	Jul 3, 2021	Knowledge DistillationModel Compression	CodeCode Available	1	5
Efficient Visual Pretraining with Contrastive Detection	Mar 19, 2021	Semantic SegmentationTransfer Learning	CodeCode Available	1	5
A deep learning framework for solution and discovery in solid mechanics	Feb 14, 2020	Transfer Learning	CodeCode Available	1	5
EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition	Mar 10, 2021	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	1	5
Learning to Transfer Examples for Partial Domain Adaptation	Mar 28, 2019	Domain AdaptationPartial Domain Adaptation	CodeCode Available	1	5
Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation	Apr 25, 2022	Continual LearningDomain Adaptation	CodeCode Available	1	5

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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