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 171–180 of 10307 papers

Title	Date	Tasks	Status	Hype
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning	Feb 13, 2023	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1
Automatic identification of segmentation errors for radiotherapy using geometric learning	Jun 27, 2022	Graph Neural NetworkSelf-Supervised Learning	CodeCode Available	1
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer Learning	Apr 25, 2020	Bayesian OptimizationTransfer Learning	CodeCode Available	1
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning	Sep 13, 2017	Cloud ComputingDeep Reinforcement Learning	CodeCode Available	1
Automatic Dialect Adaptation in Finnish and its Effect on Perceived Creativity	Sep 6, 2020	NMTTransfer Learning	CodeCode Available	1
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation	Jun 6, 2020	DecoderImage Captioning	CodeCode Available	1
AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators	Sep 21, 2021	Contrastive LearningGraph Representation Learning	CodeCode Available	1
Authorship Style Transfer with Policy Optimization	Mar 12, 2024	Style TransferTransfer Learning	CodeCode Available	1
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks	Sep 18, 2021	Meta-LearningNeural Architecture Search	CodeCode Available	1
Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets and Contrastive Molecular Explanations	Apr 13, 2022	Transfer 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