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
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing	Feb 13, 2024	Edge-computingFederated Learning	CodeCode Available	1
A Unified Framework for Domain Adaptive Pose Estimation	Apr 1, 2022	2D Pose EstimationAnimal Pose Estimation	CodeCode Available	1
A Strong and Simple Deep Learning Baseline for BCI MI Decoding	Sep 11, 2023	Deep LearningEEG	CodeCode Available	1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning	Dec 14, 2023	Computational EfficiencyFederated Learning	CodeCode Available	1
A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learning	Mar 5, 2024	Contrastive LearningTransfer Learning	CodeCode Available	1
Few-shot Image Generation via Adaptation-Aware Kernel Modulation	Oct 29, 2022	10-shot image generationDomain Adaptation	CodeCode Available	1
Byakto Speech: Real-time long speech synthesis with convolutional neural network: Transfer learning from English to Bangla	May 31, 2021	Deep Learningspeech-recognition	CodeCode Available	1
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
Broken Neural Scaling Laws	Oct 26, 2022	Adversarial RobustnessContinual Learning	CodeCode Available	1
Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models	Dec 18, 2024	Contrastive LearningKnowledge Graphs	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