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 4031–4040 of 10307 papers

Title	Date	Tasks	Status
Decision Support System for Detection and Classification of Skin Cancer using CNN	Dec 9, 2019	General ClassificationImage Augmentation	—Unverified
Automated Pruning for Deep Neural Network Compression	Dec 5, 2017	Neural Network CompressionTransfer Learning	—Unverified
Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning	Jan 29, 2021	Domain AdaptationGrammatical Error Correction	—Unverified
Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry	May 30, 2024	Fault DiagnosisFew-Shot Learning	—Unverified
A multilingual training strategy for low resource Text to Speech	Sep 2, 2024	Cross-Lingual Transfertext-to-speech	—Unverified
Gastrointestinal Disorder Detection with a Transformer Based Approach	Oct 6, 2022	image-classificationImage Classification	—Unverified
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach	Jan 1, 2021	Multi-Task LearningTransfer Learning	—Unverified
Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior	Mar 30, 2024	Transfer LearningVariable Selection	—Unverified
Deception Detection with Feature-Augmentation by soft Domain Transfer	May 1, 2023	Deception DetectionTransfer Learning	—Unverified
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach	Nov 11, 2021	Embeddings EvaluationFeature Engineering	—Unverified

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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