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 1441–1450 of 10307 papers

Title	Date	Tasks	Status	Hype
An Empirical Analysis of Image-Based Learning Techniques for Malware Classification	Mar 24, 2021	Deep LearningGeneral Classification	CodeCode Available	1
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation	Aug 16, 2021	Domain AdaptationSegmentation	CodeCode Available	1
Multi-Task Multi-Scale Contrastive Knowledge Distillation for Efficient Medical Image Segmentation	Jun 5, 2024	Contrastive LearningImage Segmentation	CodeCode Available	1
Multi-Task Reinforcement Learning with Context-based Representations	Feb 11, 2021	Multi-Task Learningreinforcement-learning	CodeCode Available	1
PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition	Jul 7, 2022	3D Shape Classification3D Shape Recognition	CodeCode Available	1
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise	Nov 20, 2017	ClassificationGeneral Classification	CodeCode Available	1
Classification of animal sounds in a hyperdiverse rainforest using Convolutional Neural Networks	Nov 29, 2021	Data AugmentationManagement	CodeCode Available	1
Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer Learning	May 22, 2020	General ClassificationTransfer Learning	CodeCode Available	1
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters	Jan 20, 2022	modelTransfer Learning	CodeCode Available	1
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks	Jun 18, 2022	Continual LearningTransfer 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