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 4491–4500 of 10307 papers

Title	Date	Tasks	Status	Hype
On the cross-lingual transferability of multilingual prototypical models across NLU tasks	Jul 19, 2022	Few-Shot LearningMeta-Learning	—Unverified	0
Supervised Contrastive ResNet and Transfer Learning for the In-vehicle Intrusion Detection System	Jul 18, 2022	Intrusion DetectionTransfer Learning	—Unverified	0
CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion Recognition	Jul 18, 2022	Cross-corpusEmotion Recognition	CodeCode Available	0
An EfficientNet-based modified sigmoid transform for enhancing dermatological macro-images of melanoma and nevi skin lesions	Jul 17, 2022	Lesion SegmentationLocal Color Enhancement	CodeCode Available	0
TSPipe: Learn from Teacher Faster with Pipelines	Jul 17, 2022	GPUKnowledge Distillation	CodeCode Available	0
Learning with Recoverable Forgetting	Jul 17, 2022	General KnowledgeTransfer Learning	CodeCode Available	1
Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation	Jul 17, 2022	Domain Adaptationdomain classification	—Unverified	0
Multi-Task and Transfer Learning for Federated Learning Applications	Jul 17, 2022	Federated LearningMeta-Learning	—Unverified	0
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery	Jul 17, 2022	Land Cover ClassificationSemantic Segmentation	CodeCode Available	2
JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes	Jul 16, 2022	Autonomous DrivingDepth Estimation	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