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 2891–2900 of 10307 papers

Title	Date	Tasks	Status	Hype
Parameter-Efficient Transfer Learning for Remote Sensing Image-Text Retrieval	Aug 24, 2023	Cross-Modal RetrievalImage-text matching	CodeCode Available	1
Motion-Guided Masking for Spatiotemporal Representation Learning	Aug 24, 2023	Domain AdaptationRepresentation Learning	—Unverified	0
Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis	Aug 24, 2023	Domain AdaptationRepresentation Learning	CodeCode Available	0
E(3)-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning	Aug 23, 2023	Inductive BiasMulti-agent Reinforcement Learning	CodeCode Available	1
Self-Supervised Learning for Endoscopic Video Analysis	Aug 23, 2023	Self-Supervised LearningSurgical phase recognition	CodeCode Available	1
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection	Aug 23, 2023	Anomaly DetectionOne-Class Classification	—Unverified	0
Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation	Aug 23, 2023	Network PruningTransfer Learning	CodeCode Available	1
Efficient Transfer Learning in Diffusion Models via Adversarial Noise	Aug 23, 2023	DenoisingDiversity	—Unverified	0
Addressing Dynamic and Sparse Qualitative Data: A Hilbert Space Embedding of Categorical Variables	Aug 22, 2023	Transfer Learning	—Unverified	0
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models	Aug 22, 2023	Few-Shot Image Classificationimage-classification	—Unverified	0

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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