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 3401–3410 of 10307 papers

Title	Date	Tasks	Status	Hype
π-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation	Apr 27, 2023	Multi-Task LearningTransfer Learning	CodeCode Available	1
Maximizing Model Generalization for Machine Condition Monitoring with Self-Supervised Learning and Federated Learning	Apr 27, 2023	Domain AdaptationFederated Learning	—Unverified	0
Deep Fast Vision: Accelerated Deep Transfer Learning Vision Prototyping and Beyond	Apr 26, 2023	AutoMLImage Classification	CodeCode Available	1
Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption	Apr 26, 2023	Privacy PreservingTransfer Learning	—Unverified	0
Exploiting CNNs for Semantic Segmentation with Pascal VOC	Apr 26, 2023	Data AugmentationSegmentation	—Unverified	0
MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis	Apr 25, 2023	Computed Tomography (CT)COVID-19 Diagnosis	—Unverified	0
Towards Compute-Optimal Transfer Learning	Apr 25, 2023	Computational EfficiencyContinual Learning	—Unverified	0
Injecting structural hints: Using language models to study inductive biases in language learning	Apr 25, 2023	Inductive BiasTransfer Learning	CodeCode Available	0
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning	Apr 25, 2023	Contrastive LearningTransfer Learning	—Unverified	0
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques	Apr 25, 2023	Active LearningAnomaly Detection	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