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 151–160 of 10307 papers

Title	Date	Tasks	Status	Hype
TorchGeo: Deep Learning With Geospatial Data	Nov 17, 2021	Deep LearningTransfer Learning	CodeCode Available	2
Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review	Nov 8, 2021	Object DetectionTransfer Learning	CodeCode Available	2
Learning Dense Representations of Phrases at Scale	Dec 23, 2020	Open-Domain Question AnsweringQuestion Answering	CodeCode Available	2
Feature Learning in Infinite-Width Neural Networks	Nov 30, 2020	Few-Shot LearningTransfer Learning	CodeCode Available	2
Deep learning for time series classification	Oct 1, 2020	Activity RecognitionClassification	CodeCode Available	2
Abstractive Summarization of Spoken andWritten Instructions with BERT	Aug 21, 2020	Abstractive Text SummarizationArticles	CodeCode Available	2
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation	Jul 27, 2020	Face GenerationTransfer Learning	CodeCode Available	2
TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning	Jul 22, 2020	Transfer Learning	CodeCode Available	2
Simplifying Paragraph-level Question Generation via Transformer Language Models	May 3, 2020	Language ModelingLanguage Modelling	CodeCode Available	2
AdapterFusion: Non-Destructive Task Composition for Transfer Learning	May 1, 2020	Language ModellingMulti-Task Learning	CodeCode Available	2

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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