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 3341–3350 of 10307 papers

Title	Date	Tasks	Status	Score
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection	Nov 21, 2021	Anomaly DetectionMulti-Task 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
Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer Learning	Dec 10, 2020	Image RetrievalPose Estimation	CodeCode Available	5
Straightforward Layer-wise Pruning for More Efficient Visual Adaptation	Jul 19, 2024	ClusteringTransfer Learning	CodeCode Available	5
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing Supervision	Oct 8, 2023	Transfer Learning	CodeCode Available	5
Effects of the Nonlinearity in Activation Functions on the Performance of Deep Learning Models	Oct 14, 2020	image-classificationImage Classification	CodeCode Available	5
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning	Jan 19, 2025	Brain Tumor SegmentationImage Segmentation	CodeCode Available	5
Enhancing Dataset Distillation via Non-Critical Region Refinement	Mar 24, 2025	Dataset DistillationTransfer Learning	CodeCode Available	5
Structured Probabilistic Pruning for Convolutional Neural Network Acceleration	Sep 20, 2017	Transfer 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

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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