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 2051–2060 of 10307 papers

Title	Date	Tasks	Status
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images	Jun 5, 2021	Data AugmentationTransfer Learning	—Unverified
AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors	Feb 15, 2025	Representation LearningTransfer Learning	—Unverified
CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network	Aug 18, 2018	ClassificationGeneral Classification	—Unverified
Adversarial Imitation via Variational Inverse Reinforcement Learning	Sep 17, 2018	reinforcement-learningReinforcement Learning	—Unverified
Cross-Lingual Transfer Learning for Speech Translation	Jul 1, 2024	Cross-Lingual TransferDecoder	—Unverified
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection	Jun 1, 2022	Cell DetectionTransfer Learning	—Unverified
Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset	Jun 3, 2019	Transfer Learning	—Unverified
Any-Shot Sequential Anomaly Detection in Surveillance Videos	Apr 5, 2020	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks	Nov 10, 2021	Disease PredictionTransfer Learning	—Unverified
Adversarial Fine-tune with Dynamically Regulated Adversary	Apr 28, 2022	Adversarial AttackAdversarial Robustness	—Unverified

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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