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 2501–2510 of 10307 papers

Title	Date	Tasks	Status	Hype
Towards interpretable-by-design deep learning algorithms	Nov 19, 2023	class-incremental learningClass Incremental Learning	—Unverified	0
Open Set Dandelion Network for IoT Intrusion Detection	Nov 19, 2023	Domain AdaptationIntrusion Detection	—Unverified	0
Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models	Nov 18, 2023	Computational EfficiencyDimensionality Reduction	—Unverified	0
Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names	Nov 18, 2023	Transfer Learning	—Unverified	0
Towards Robust and Accurate Visual Prompting	Nov 18, 2023	Adversarial RobustnessTransfer Learning	—Unverified	0
Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning	Nov 18, 2023	Transfer Learning	CodeCode Available	4
Using Guided Transfer Learning to Predispose AI Agent to Learn Efficiently from Small RNA-sequencing Datasets	Nov 17, 2023	AI AgentFew-Shot Learning	—Unverified	0
TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representation	Nov 17, 2023	Drug Response PredictionTransfer Learning	CodeCode Available	0
Physics-Enhanced Multi-fidelity Learning for Optical Surface Imprint	Nov 17, 2023	Transfer Learning	—Unverified	0
SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing	Nov 17, 2023	Hyperspectral UnmixingTransfer Learning	—Unverified	0

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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