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 2941–2950 of 10307 papers

Title	Date	Tasks	Status	Hype
Smart Knowledge Transfer using Google-like Search	Aug 12, 2023	Transfer Learning	—Unverified	0
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models	Aug 12, 2023	Federated Learningparameter-efficient fine-tuning	—Unverified	0
Monitoring of Urban Changes with multi-modal Sentinel 1 and 2 Data in Mariupol, Ukraine, in 2022/23	Aug 11, 2023	Transfer Learning	CodeCode Available	0
Classification of All Blood Cell Images using ML and DL Models	Aug 11, 2023	AllClassification	—Unverified	0
Tweet Sentiment Extraction using Viterbi Algorithm with Transfer Learning	Aug 11, 2023	SentenceTransfer Learning	CodeCode Available	0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation	Aug 11, 2023	Anomaly DetectionData Poisoning	—Unverified	0
Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance	Aug 11, 2023	Transfer Learning	—Unverified	0
Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning	Aug 10, 2023	Transfer Learning	—Unverified	0
Multi-domain Recommendation with Embedding Disentangling and Domain Alignment	Aug 10, 2023	Transfer Learning	CodeCode Available	1
Spatio-Temporal Encoding of Brain Dynamics with Surface Masked Autoencoders	Aug 10, 2023	Transfer 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