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 4091–4100 of 10307 papers

Title	Date	Tasks	Status	Hype
Amplifying Membership Exposure via Data Poisoning	Nov 1, 2022	Data PoisoningOverall - Test	CodeCode Available	1
Effective Cross-Task Transfer Learning for Explainable Natural Language Inference with T5	Oct 31, 2022	Multi-Task LearningNatural Language Inference	CodeCode Available	0
Confidence-Nets: A Step Towards better Prediction Intervals for regression Neural Networks on small datasets	Oct 31, 2022	PredictionPrediction Intervals	—Unverified	0
Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples	Oct 31, 2022	3D Anomaly DetectionAnomaly Detection	—Unverified	0
Teacher-student curriculum learning for reinforcement learning	Oct 31, 2022	Board GamesDecision Making	—Unverified	0
Improving Cause-of-Death Classification from Verbal Autopsy Reports	Oct 31, 2022	ClassificationDomain Adaptation	—Unverified	0
Actionable Phrase Detection using NLP	Oct 30, 2022	Actionable Phrase DetectionSentence	—Unverified	0
Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning	Oct 30, 2022	Question AnsweringTransfer Learning	CodeCode Available	0
A simple, efficient and scalable contrastive masked autoencoder for learning visual representations	Oct 30, 2022	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1
Few-shot Image Generation via Adaptation-Aware Kernel Modulation	Oct 29, 2022	10-shot image generationDomain Adaptation	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