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 1271–1280 of 10307 papers

Title	Date	Tasks	Status	Hype
Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks	Jul 24, 2020	Blood pressure estimationManagement	CodeCode Available	1
Rethinking CNN Models for Audio Classification	Jul 22, 2020	Audio ClassificationClassification	CodeCode Available	1
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding	Jul 21, 2020	Point Cloud Pre-trainingRepresentation Learning	CodeCode Available	1
Generative Hierarchical Features from Synthesizing Images	Jul 20, 2020	Face Verificationimage-classification	CodeCode Available	1
NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search	Jul 20, 2020	Image ClassificationNeural Architecture Search	CodeCode Available	1
Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning	Jul 19, 2020	AttributeGeneralized Zero-Shot Learning	CodeCode Available	1
AquaVision: Automating the detection of waste in water bodies using deep transfer learning	Jul 18, 2020	object-detectionObject Detection	CodeCode Available	1
Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data	Jul 16, 2020	BIG-bench Machine LearningClustering	CodeCode Available	1
Do Adversarially Robust ImageNet Models Transfer Better?	Jul 16, 2020	Object RecognitionTransfer Learning	CodeCode Available	1
On Robustness and Transferability of Convolutional Neural Networks	Jul 16, 2020	image-classificationImage Classification	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