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 2291–2300 of 10307 papers

Title	Date	Tasks	Status
BreakingNews: Article Annotation by Image and Text Processing	Mar 23, 2016	ArticlesImage Retrieval	—Unverified
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization	Mar 1, 2023	Self-Supervised LearningTransfer Learning	—Unverified
A Concise Review of Transfer Learning	Apr 5, 2021	Transfer Learning	—Unverified
A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers	May 30, 2024	CryptanalysisMemorization	—Unverified
A Domain Adaptation Regularization for Denoising Autoencoders	Aug 1, 2016	DenoisingDocument Ranking	—Unverified
An information-Theoretic Approach to Semi-supervised Transfer Learning	Jun 11, 2023	Transfer Learning	—Unverified
Brain Tumor Detection Using Deep Learning Approaches	Sep 21, 2023	Deep LearningTransfer Learning	—Unverified
Tuned Inception V3 for Recognizing States of Cooking Ingredients	May 5, 2019	Transfer Learning	—Unverified
Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities	Jun 29, 2020	Transfer Learning	—Unverified
Deep Embedding Kernel	Apr 16, 2018	Deep LearningDimensionality Reduction	—Unverified

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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