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 961–970 of 10307 papers

Title	Date	Tasks	Status	Hype	Score
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations	Jun 19, 2023	Domain AdaptationRetinal Vessel Segmentation	CodeCode Available	1	5
Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resources	Sep 18, 2024	GPULanguage Modeling	CodeCode Available	1	5
Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning	Apr 13, 2022	Cross-Lingual TransferLanguage Modelling	CodeCode Available	1	5
MultiLoKo: a multilingual local knowledge benchmark for LLMs spanning 31 languages	Apr 14, 2025	Transfer Learning	CodeCode Available	1	5
Multi-Modality is All You Need for Transferable Recommender Systems	Dec 15, 2023	AllContrastive Learning	CodeCode Available	1	5
Multimodal Parameter-Efficient Few-Shot Class Incremental Learning	Mar 8, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
Drug and Disease Interpretation Learning with Biomedical Entity Representation Transformer	Jan 22, 2021	Drug DiscoveryMetric Learning	CodeCode Available	1	5
Dynamic Domain Adaptation for Efficient Inference	Mar 26, 2021	Domain AdaptationDomain Generalization	CodeCode Available	1	5
Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets and Contrastive Molecular Explanations	Apr 13, 2022	Transfer Learning	CodeCode Available	1	5
Adapting LLaMA Decoder to Vision Transformer	Apr 10, 2024	Computational EfficiencyDecoder	CodeCode Available	1	5

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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