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 441–450 of 10307 papers

Title	Date	Tasks	Status	Hype	Score
Automatic Dialect Adaptation in Finnish and its Effect on Perceived Creativity	Sep 6, 2020	NMTTransfer Learning	CodeCode Available	1	5
Determining Chess Game State From an Image	Apr 30, 2021	Transfer Learning	CodeCode Available	1	5
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	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
Active Learning for Domain Adaptation: An Energy-Based Approach	Dec 2, 2021	Active LearningDomain Adaptation	CodeCode Available	1	5
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning	Feb 13, 2023	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Neuro2Semantic: A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEG	May 31, 2025	EEGText Generation	CodeCode Available	1	5
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation	Jun 6, 2020	DecoderImage Captioning	CodeCode Available	1	5
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning	Sep 12, 2022	Federated Learningimage-classification	CodeCode Available	1	5
Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning	Feb 19, 2020	Transfer Learning	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