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 1101–1110 of 10307 papers

Title	Date	Tasks	Status	Hype	Score
Fine-tuning giant neural networks on commodity hardware with automatic pipeline model parallelism	Jul 14, 2021	GPUTransfer Learning	CodeCode Available	1	5
Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns	Dec 21, 2023	Graph ClassificationGraph Learning	CodeCode Available	1	5
A systematic approach to deep learning-based nodule detection in chest radiographs	Jun 21, 2023	Data AugmentationLung Nodule Detection	CodeCode Available	1	5
A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis	Aug 12, 2021	BenchmarkingMedical Image Analysis	CodeCode Available	1	5
Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEs	Dec 7, 2024	Transfer Learning	CodeCode Available	1	5
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification	Jun 17, 2022	Federated LearningFew-Shot Learning	CodeCode Available	1	5
Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones	Mar 10, 2021	Knowledge Distillationobject-detection	CodeCode Available	1	5
Equivariant Graph Neural Networks for 3D Macromolecular Structure	Jun 7, 2021	Atom3D benchmarkBIG-bench Machine Learning	CodeCode Available	1	5
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement	Sep 20, 2021	Image-level Supervised Instance SegmentationInstance Segmentation	CodeCode Available	1	5
Evaluating histopathology transfer learning with ChampKit	Jun 14, 2022	BenchmarkingCell Detection	CodeCode Available	1	5

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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