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 2081–2090 of 10307 papers

Title	Date	Tasks	Status	Score
Geostatistical Learning: Challenges and Opportunities	Feb 17, 2021	Learning TheoryModel Selection	CodeCode Available	5
Getting aligned on representational alignment	Oct 18, 2023	NavigateTransfer Learning	CodeCode Available	5
Boosting Data Analytics With Synthetic Volume Expansion	Oct 27, 2023	Sentiment AnalysisSynthetic Data Generation	CodeCode Available	5
AdeNet: Deep learning architecture that identifies damaged electrical insulators in power lines	Mar 2, 2021	Anomaly DetectionTransfer Learning	CodeCode Available	5
GIST at SemEval-2018 Task 12: A network transferring inference knowledge to Argument Reasoning Comprehension task	Jun 1, 2018	Common Sense ReasoningNatural Language Inference	CodeCode Available	5
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation	May 17, 2022	Low-Resource Neural Machine TranslationMachine Translation	CodeCode Available	5
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery	Nov 14, 2024	image-classificationImage Classification	CodeCode Available	5
Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation	Nov 26, 2024	Data-free Knowledge DistillationDiversity	CodeCode Available	5
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction	Nov 14, 2018	Band GapBIG-bench Machine Learning	CodeCode Available	5
General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning	May 13, 2018	Deep Reinforcement LearningReinforcement Learning	CodeCode Available	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