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	Hype
The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation	May 16, 2023	Abstractive Text SummarizationContinual Learning	CodeCode Available	0
Transfer Learning for Causal Effect Estimation	May 16, 2023	regressionTransfer Learning	—Unverified	0
Privacy-Preserving Ensemble Infused Enhanced Deep Neural Network Framework for Edge Cloud Convergence	May 16, 2023	Ensemble LearningPrivacy Preserving	—Unverified	0
Osteosarcoma Tumor Detection using Transfer Learning Models	May 16, 2023	Cell DetectionTransfer Learning	—Unverified	0
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images	May 16, 2023	Medical DiagnosisTransfer Learning	—Unverified	0
Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion	May 16, 2023	Deep Reinforcement Learningreinforcement-learning	—Unverified	0
Deep Ensembling for Perceptual Image Quality Assessment	May 16, 2023	Blind Image Quality AssessmentImage Quality Assessment	—Unverified	0
A Whisper transformer for audio captioning trained with synthetic captions and transfer learning	May 15, 2023	Audio captioningSpeech-to-Text	CodeCode Available	1
Self-Supervised Pretraining on Paired Sequences of fMRI Data for Transfer Learning to Brain Decoding Tasks	May 15, 2023	Brain DecodingTransfer Learning	—Unverified	0
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding	May 15, 2023	DiversityTransfer Learning	CodeCode Available	1

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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