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 4741–4750 of 10307 papers

Title	Date	Tasks	Status
A Survey of Deep Visual Cross-Domain Few-Shot Learning	Mar 16, 2023	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold	Feb 16, 2023	Transfer Learning	—Unverified
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4	Oct 4, 2023	Data AugmentationSelf-Supervised Learning	—Unverified
Learning structures of the French clinical language:development and validation of word embedding models using 21 million clinical reports from electronic health records	Jul 26, 2022	Language ModellingTransfer Learning	—Unverified
Learning Student-Friendly Teacher Networks for Knowledge Distillation	Feb 12, 2021	Knowledge DistillationTransfer Learning	—Unverified
Learning Task Automata for Reinforcement Learning using Hidden Markov Models	Aug 25, 2022	reinforcement-learningReinforcement Learning	—Unverified
Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties	Oct 18, 2013	Transfer Learning	—Unverified
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition	Mar 17, 2024	Action RecognitionActivity Recognition	—Unverified
Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization	Mar 21, 2018	BIG-bench Machine LearningData Augmentation	—Unverified
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community	Sep 1, 2017	speech-recognitionSpeech Recognition	—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