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 441–450 of 10307 papers

Title	Date	Tasks	Status	Hype
DeezyMatch: A Flexible Deep Learning Approach to Fuzzy String Matching	Oct 1, 2020	Deep LearningTransfer Learning	CodeCode Available	1
DeiT III: Revenge of the ViT	Apr 14, 2022	Data Augmentationimage-classification	CodeCode Available	1
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	CodeCode Available	1
Denoised Self-Augmented Learning for Social Recommendation	May 22, 2023	Self-Supervised LearningTransfer Learning	CodeCode Available	1
Active Learning for Domain Adaptation: An Energy-Based Approach	Dec 2, 2021	Active LearningDomain Adaptation	CodeCode Available	1
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty	May 4, 2023	Knowledge Distillationobject-detection	CodeCode Available	1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning	Feb 13, 2023	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1
Automatic identification of segmentation errors for radiotherapy using geometric learning	Jun 27, 2022	Graph Neural NetworkSelf-Supervised Learning	CodeCode Available	1
Detecting Omissions in Geographic Maps through Computer Vision	Jul 15, 2024	Transfer Learning	CodeCode Available	1
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer Learning	Apr 25, 2020	Bayesian OptimizationTransfer 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