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 451–460 of 10307 papers

Title	Date	Tasks	Status	Hype
Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation	Aug 23, 2023	Network PruningTransfer Learning	CodeCode Available	1
Self-Supervised Learning for Endoscopic Video Analysis	Aug 23, 2023	Self-Supervised LearningSurgical phase recognition	CodeCode Available	1
E(3)-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning	Aug 23, 2023	Inductive BiasMulti-agent Reinforcement Learning	CodeCode Available	1
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation	Aug 22, 2023	Contrastive LearningDecoder	CodeCode Available	1
Diffusion Model as Representation Learner	Aug 21, 2023	Denoisingimage-classification	CodeCode Available	1
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning	Aug 21, 2023	Federated LearningKnowledge Distillation	CodeCode Available	1
PEvoLM: Protein Sequence Evolutionary Information Language Model	Aug 16, 2023	Language ModelingLanguage Modelling	CodeCode Available	1
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation	Aug 16, 2023	Multi-Task LearningRecommendation Systems	CodeCode Available	1
Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models	Aug 15, 2023	Image SegmentationMedical Image Segmentation	CodeCode Available	1
Multi-domain Recommendation with Embedding Disentangling and Domain Alignment	Aug 10, 2023	Transfer 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