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 4511–4520 of 10307 papers

Title	Date	Tasks	Status	Hype
A Data-Based Perspective on Transfer Learning	Jul 12, 2022	Transfer Learning	CodeCode Available	1
Transferability-Guided Cross-Domain Cross-Task Transfer Learning	Jul 12, 2022	Transfer Learning	—Unverified	0
Scaling Novel Object Detection with Weakly Supervised Detection Transformers	Jul 11, 2022	Multiple Instance LearningNovel Object Detection	CodeCode Available	1
Real-Time And Robust 3D Object Detection with Roadside LiDARs	Jul 11, 2022	3D Object DetectionAutonomous Driving	—Unverified	0
An Interpretable Joint Nonnegative Matrix Factorization-Based Point Cloud Distance Measure	Jul 11, 2022	DenoisingTransfer Learning	—Unverified	0
Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognition	Jul 11, 2022	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	0
Dual-Correction Adaptation Network for Noisy Knowledge Transfer	Jul 10, 2022	Domain AdaptationTransfer Learning	—Unverified	0
Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain	Jul 8, 2022	Land Cover Classificationobject-detection	CodeCode Available	1
On Improving the Performance of Glitch Classification for Gravitational Wave Detection by using Generative Adversarial Networks	Jul 8, 2022	ClassificationData Augmentation	—Unverified	0
Beyond Transfer Learning: Co-finetuning for Action Localisation	Jul 8, 2022	Transfer LearningVideo Classification	—Unverified	0

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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