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 3371–3380 of 10307 papers

Title	Date	Tasks	Status	Hype
Shotgun crystal structure prediction using machine-learned formation energies	May 3, 2023	PredictionTransfer Learning	CodeCode Available	1
DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents	May 3, 2023	Few-Shot LearningLanguage Identification	CodeCode Available	0
Improving Contrastive Learning of Sentence Embeddings from AI Feedback	May 3, 2023	Contrastive LearningData Augmentation	CodeCode Available	1
VPGTrans: Transfer Visual Prompt Generator across LLMs	May 2, 2023	GPUTransfer Learning	CodeCode Available	2
Improving Cancer Hallmark Classification with BERT-based Deep Learning Approach	May 2, 2023	Deep LearningTransfer Learning	—Unverified	0
Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based Alignment	May 2, 2023	Cross-Lingual TransferFew-Shot Learning	CodeCode Available	0
Detect, Distill and Update: Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data	May 1, 2023	Knowledge DistillationSynthetic Data Generation	CodeCode Available	0
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation	May 1, 2023	Contrastive LearningLanguage Modelling	—Unverified	0
Deception Detection with Feature-Augmentation by soft Domain Transfer	May 1, 2023	Deception DetectionTransfer Learning	—Unverified	0
Towards Unbiased Training in Federated Open-world Semi-supervised Learning	May 1, 2023	Open-World Semi-Supervised LearningTransfer Learning	—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