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 481–490 of 10307 papers

Title	Date	Tasks	Status	Hype	Score
CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models	May 23, 2023	2kMath	CodeCode Available	1	5
Efficient parametrization of multi-domain deep neural networks	Mar 27, 2018	Continual LearningTransfer Learning	CodeCode Available	1	5
Decoupling Representation and Classifier for Long-Tailed Recognition	Oct 21, 2019	ClassificationGeneral Classification	CodeCode Available	1	5
Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets and Contrastive Molecular Explanations	Apr 13, 2022	Transfer Learning	CodeCode Available	1	5
AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning	Sep 3, 2020	BIG-bench Machine LearningTransfer Learning	CodeCode Available	1	5
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts	Feb 3, 2018	Active LearningTransfer Learning	CodeCode Available	1	5
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions	May 24, 2019	Question AnsweringReading Comprehension	CodeCode Available	1	5
A Further Study of Unsupervised Pre-training for Transformer Based Speech Recognition	May 20, 2020	speech-recognitionSpeech Recognition	CodeCode Available	1	5
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data	Sep 19, 2020	Multi-Task LearningNatural Language Inference	CodeCode Available	1	5
An Improved Person Re-identification Method by light-weight convolutional neural network	Aug 21, 2020	Person Re-IdentificationTransfer Learning	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