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 1421–1430 of 10307 papers

Title	Date	Tasks	Status	Hype
On Leveraging Pretrained GANs for Generation with Limited Data	Feb 26, 2020	Image GenerationTransfer Learning	CodeCode Available	1
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs	Feb 25, 2020	10-shot image generationImage Generation	CodeCode Available	1
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients	Feb 21, 2020	Imitation LearningTransfer Learning	CodeCode Available	1
The continuous categorical: a novel simplex-valued exponential family	Feb 20, 2020	Neural Network CompressionTransfer Learning	CodeCode Available	1
Distance-Based Regularisation of Deep Networks for Fine-Tuning	Feb 19, 2020	Transfer Learning	CodeCode Available	1
Rethinking the Hyperparameters for Fine-tuning	Feb 19, 2020	Transfer Learning	CodeCode Available	1
Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning	Feb 19, 2020	Transfer Learning	CodeCode Available	1
SentenceMIM: A Latent Variable Language Model	Feb 18, 2020	Language ModelingLanguage Modelling	CodeCode Available	1
A deep learning framework for solution and discovery in solid mechanics	Feb 14, 2020	Transfer Learning	CodeCode Available	1
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization	Feb 11, 2020	Combinatorial OptimizationHyperparameter Optimization	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