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 1311–1320 of 10307 papers

Title	Date	Tasks	Status	Hype
Inductive Matrix Completion Based on Graph Neural Networks	Apr 26, 2019	Graph Neural NetworkMatrix Completion	CodeCode Available	1
Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial Settings	Jun 14, 2024	Transfer Learning	CodeCode Available	1
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning	Oct 8, 2020	Reinforcement Learning (RL)Transfer Learning	CodeCode Available	1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning	Feb 13, 2023	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1
Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones	Mar 16, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
Intra-Inter Camera Similarity for Unsupervised Person Re-Identification	Mar 22, 2021	Person Re-IdentificationPseudo Label	CodeCode Available	1
Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets	Oct 22, 2020	ArticlesBenchmarking	CodeCode Available	1
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference	Jun 7, 2021	Cross-Lingual TransferNatural Language Inference	CodeCode Available	1
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from Transformer	May 24, 2023	General KnowledgeTransfer Learning	CodeCode Available	1
CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning	Dec 11, 2020	Electron Microscopy Image SegmentationSegmentation	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