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 4321–4330 of 10307 papers

Title	Date	Tasks	Status	Hype
Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence Function	Sep 14, 2022	Decision MakingDeep Reinforcement Learning	CodeCode Available	0
Language Chameleon: Transformation analysis between languages using Cross-lingual Post-training based on Pre-trained language models	Sep 14, 2022	Cross-Lingual TransferTransfer Learning	—Unverified	0
SFS-A68: a dataset for the segmentation of space functions in apartment buildings	Sep 13, 2022	ClassificationImage Segmentation	CodeCode Available	0
Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican	Sep 13, 2022	Cross-Lingual TransferMachine Translation	—Unverified	0
Learning ASR pathways: A sparse multilingual ASR model	Sep 13, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy	Sep 13, 2022	Generalization BoundsTransfer Learning	—Unverified	0
Identification of Cognitive Workload during Surgical Tasks with Multimodal Deep Learning	Sep 12, 2022	AnatomyEEG	—Unverified	0
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning	Sep 12, 2022	Federated Learningimage-classification	CodeCode Available	1
Automatically Score Tissue Images Like a Pathologist by Transfer Learning	Sep 9, 2022	Learning TheoryTransfer Learning	—Unverified	0
Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health	Sep 9, 2022	Multi-Armed BanditsTransfer Learning	—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