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 4311–4320 of 10307 papers

Title	Date	Tasks	Status	Hype
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-Learning	Sep 19, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	0
Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle	Sep 19, 2022	Domain AdaptationRepresentation Learning	CodeCode Available	1
Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features	Sep 18, 2022	Age-Invariant Face RecognitionClassification	—Unverified	0
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning	Sep 18, 2022	Data AugmentationSelf-Supervised Learning	—Unverified	0
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks	Sep 16, 2022	Deep Reinforcement LearningManagement	CodeCode Available	1
An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer Learning	Sep 16, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0
Top-Tuning: a study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods	Sep 16, 2022	image-classificationImage Classification	—Unverified	0
ScreenQA: Large-Scale Question-Answer Pairs over Mobile App Screenshots	Sep 16, 2022	BenchmarkingQuestion Answering	CodeCode Available	1
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning	Sep 16, 2022	Anomaly DetectionTransfer Learning	—Unverified	0
Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning	Sep 16, 2022	3D Semantic SegmentationPseudo Label	CodeCode Available	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