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 4271–4280 of 10307 papers

Title	Date	Tasks	Status	Hype
基于知识迁移的情感-原因对抽取(Emotion-Cause Pair Extraction Based on Knowledge-Transfer)	Oct 1, 2022	Emotion-Cause Pair ExtractionTransfer Learning	—Unverified	0
Supervised Contrastive Learning for Cross-lingual Transfer Learning	Oct 1, 2022	Contrastive LearningCross-Lingual Transfer	—Unverified	0
Transfer Learning for Individual Treatment Effect Estimation	Oct 1, 2022	Causal Inferencecounterfactual	—Unverified	0
Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System	Sep 30, 2022	Decision MakingTransfer Learning	—Unverified	0
Evaluation of taxonomic and neural embedding methods for calculating semantic similarity	Sep 30, 2022	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods	Sep 30, 2022	Computational EfficiencyData Augmentation	—Unverified	0
Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models	Sep 30, 2022	Multi-Task Learningparameter estimation	—Unverified	0
Towards a Unified View of Affinity-Based Knowledge Distillation	Sep 30, 2022	image-classificationImage Classification	—Unverified	0
How to tackle an emerging topic? Combining strong and weak labels for Covid news NER	Sep 29, 2022	named-entity-recognitionNamed Entity Recognition	CodeCode Available	0
3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation	Sep 29, 2022	Image SegmentationMedical Image Segmentation	CodeCode Available	2

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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