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 1401–1410 of 10307 papers

Title	Date	Tasks	Status	Hype
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning	Jun 23, 2020	Knowledge GraphsMulti-Task Learning	CodeCode Available	1
MineGAN: effective knowledge transfer from GANs to target domains with few images	Dec 11, 2019	Transfer Learning	CodeCode Available	1
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imagery	May 15, 2024	Active LearningShadow Detection	CodeCode Available	1
MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems	Sep 25, 2020	Dialogue State TrackingMulti-domain Dialogue State Tracking	CodeCode Available	1
Mitigating the Position Bias of Transformer Models in Passage Re-Ranking	Jan 18, 2021	Passage Re-RankingPosition	CodeCode Available	1
Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains	Feb 9, 2023	Transfer Learning	CodeCode Available	1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning Paradigms	Feb 22, 2023	backdoor defenseSelf-Supervised Learning	CodeCode Available	1
CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis	Oct 19, 2021	3D-Aware Image SynthesisImage Generation	CodeCode Available	1
Choquet Integral and Coalition Game-based Ensemble of Deep Learning Models for COVID-19 Screening from Chest X-ray Images	Sep 9, 2021	COVID-19 DiagnosisEnsemble Learning	CodeCode Available	1
Chip Placement with Deep Reinforcement Learning	Apr 22, 2020	Deep Reinforcement Learningreinforcement-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