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 3321–3330 of 10307 papers

Title	Date	Tasks	Status	Score
Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks	Dec 11, 2024	Drug DiscoveryPrediction	CodeCode Available	5
Enhancing Dataset Distillation via Non-Critical Region Refinement	Mar 24, 2025	Dataset DistillationTransfer Learning	CodeCode Available	5
SpaceQA: Answering Questions about the Design of Space Missions and Space Craft Concepts	Oct 7, 2022	Reading ComprehensionTransfer Learning	CodeCode Available	5
Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge Transfer	Dec 20, 2024	Classifier calibrationFew-Shot Semantic Segmentation	CodeCode Available	5
Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer Learning	Mar 24, 2025	Continual LearningDeep Reinforcement Learning	CodeCode Available	5
Continual Prune-and-Select: Class-incremental learning with specialized subnetworks	Aug 9, 2022	class-incremental learningClass Incremental Learning	CodeCode Available	5
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion	Apr 3, 2017	Face RecognitionTransfer Learning	CodeCode Available	5
End-to-End Video Question-Answer Generation with Generator-Pretester Network	Jan 5, 2021	Answer GenerationQuestion-Answer-Generation	CodeCode Available	5
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning	Jan 19, 2025	Brain Tumor SegmentationImage Segmentation	CodeCode Available	5
End-to-End Deep Learning of Optimization Heuristics	Nov 2, 2017	Deep LearningGPU	CodeCode Available	5

Show:10 25 50

← PrevPage 333 of 1031Next →

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