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 1911–1920 of 10307 papers

Title	Date	Tasks	Status	Score
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions	Apr 1, 2023	Action RecognitionContrastive Learning	CodeCode Available	5
Advancing Adversarial Suffix Transfer Learning on Aligned Large Language Models	Aug 27, 2024	Red TeamingTransfer Learning	CodeCode Available	5
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization	Dec 26, 2017	Action ClassificationAction Localization	CodeCode Available	5
HANA: A HAndwritten NAme Database for Offline Handwritten Text Recognition	Jan 22, 2021	Handwritten Text RecognitionTransfer Learning	CodeCode Available	5
GTNet: Generative Transfer Network for Zero-Shot Object Detection	Jan 19, 2020	Generative Adversarial NetworkObject	CodeCode Available	5
Guided Transfer Learning	Mar 26, 2023	Transfer Learning	CodeCode Available	5
ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance Labeling	Feb 21, 2024	MMLURetrieval	CodeCode Available	5
Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling	Jun 2, 2022	Domain AdaptationPseudo Label	CodeCode Available	5
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation	Feb 10, 2024	Brain Tumor SegmentationImage Segmentation	CodeCode Available	5
Growing Neural Network with Shared Parameter	Jan 17, 2022	Transfer Learning	CodeCode Available	5

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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