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 2631–2640 of 10307 papers

Title	Date	Tasks	Status
Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities	Aug 25, 2024	Brain Tumor SegmentationSegmentation	—Unverified
Additional Positive Enables Better Representation Learning for Medical Images	May 31, 2023	Representation LearningSelf-Supervised Learning	—Unverified
Balanced Multi-modal Federated Learning via Cross-Modal Infiltration	Dec 31, 2023	Distributed ComputingFederated Learning	—Unverified
Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models	Feb 17, 2025	In-Context LearningSemantic Similarity	—Unverified
An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI	May 21, 2025	Transfer Learning	—Unverified
Anaphoric Zero Pronoun Identification: A Multilingual Approach	Dec 1, 2020	Transfer Learning	—Unverified
Balanced Distribution Adaptation for Transfer Learning	Jul 2, 2018	Transfer Learning	—Unverified
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification	Jan 26, 2024	Data Augmentationimage-classification	—Unverified
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning	Sep 4, 2020	Transfer Learning	—Unverified
Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model	Nov 2, 2021	Binary ClassificationHate Speech Detection	—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