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 4211–4220 of 10307 papers

Title	Date	Tasks	Status	Hype
The effect of variable labels on deep learning models trained to predict breast density	Oct 8, 2022	PredictionTransfer Learning	—Unverified	0
Training Deep Learning Algorithms on Synthetic Forest Images for Tree Detection	Oct 8, 2022	Deep LearningDiversity	CodeCode Available	1
Evaluating the Performance of StyleGAN2-ADA on Medical Images	Oct 7, 2022	Computed Tomography (CT)Data Augmentation	—Unverified	0
Explainable AI based Glaucoma Detection using Transfer Learning and LIME	Oct 7, 2022	Transfer Learning	—Unverified	0
Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters	Oct 7, 2022	DecoderResponse Generation	—Unverified	0
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization	Oct 7, 2022	Knowledge Distillationspeaker-diarization	—Unverified	0
SpaceQA: Answering Questions about the Design of Space Missions and Space Craft Concepts	Oct 7, 2022	Reading ComprehensionTransfer Learning	CodeCode Available	0
Gastrointestinal Disorder Detection with a Transformer Based Approach	Oct 6, 2022	image-classificationImage Classification	—Unverified	0
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data	Oct 6, 2022	Decision MakingDiagnostic	—Unverified	0
Biological neurons act as generalization filters in reservoir computing	Oct 6, 2022	DecoderTime Series Analysis	—Unverified	0

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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