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 1231–1240 of 10307 papers

Title	Date	Tasks	Status	Hype	Score
Continual Learning with Knowledge Transfer for Sentiment Classification	Dec 18, 2021	ClassificationContinual Learning	CodeCode Available	1	5
A Unified Framework for Domain Adaptive Pose Estimation	Apr 1, 2022	2D Pose EstimationAnimal Pose Estimation	CodeCode Available	1	5
Learning Affordance Grounding from Exocentric Images	Mar 18, 2022	DiversityHuman-Object Interaction Detection	CodeCode Available	1	5
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification	Jun 20, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1	5
Finetune like you pretrain: Improved finetuning of zero-shot vision models	Dec 1, 2022	DescriptiveFew-Shot Learning	CodeCode Available	1	5
Continual Learning via Local Module Composition	Nov 15, 2021	Continual LearningTransfer Learning	CodeCode Available	1	5
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks	Oct 25, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1	5
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions	Feb 28, 2023	Transfer Learning	CodeCode Available	1	5
On Tiny Episodic Memories in Continual Learning	Feb 27, 2019	Class Incremental LearningContinual Learning	CodeCode Available	1	5
A Strong and Simple Deep Learning Baseline for BCI MI Decoding	Sep 11, 2023	Deep LearningEEG	CodeCode Available	1	5

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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