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 4421–4430 of 10307 papers

Title	Date	Tasks	Status
Localizing the conceptual difference of two scenes using deep learning for house keeping usages	Aug 9, 2022	Transfer Learning	—Unverified
Continual Prune-and-Select: Class-incremental learning with specialized subnetworks	Aug 9, 2022	class-incremental learningClass Incremental Learning	CodeCode Available
Comparison of semi-supervised learning methods for High Content Screening quality control	Aug 9, 2022	Drug DiscoveryTransfer Learning	—Unverified
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2	Aug 9, 2022	Lifelong learningManagement	—Unverified
Object Detection Using Sim2Real Domain Randomization for Robotic Applications	Aug 8, 2022	GPUobject-detection	—Unverified
SKDCGN: Source-free Knowledge Distillation of Counterfactual Generative Networks using cGANs	Aug 8, 2022	counterfactualKnowledge Distillation	CodeCode Available
Continual Learning for Tumor Classification in Histopathology Images	Aug 7, 2022	ClassificationContinual Learning	—Unverified
A Game-Theoretic Perspective of Generalization in Reinforcement Learning	Aug 7, 2022	Few-Shot LearningMeta-Learning	—Unverified
Preserving Fine-Grain Feature Information in Classification via Entropic Regularization	Aug 7, 2022	ClassificationDiversity	CodeCode Available
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks	Aug 7, 2022	Adversarial RobustnessRobust classification	—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