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 3191–3200 of 10307 papers

Title	Date	Tasks	Status
Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes	Feb 15, 2022	Anomaly DetectionBinary Classification	—Unverified
Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals	Feb 7, 2023	Automatic Sleep Stage ClassificationClassification	—Unverified
A Comparison of Methods for Neural Network Aggregation	Mar 6, 2023	Deep LearningEnsemble Learning	—Unverified
Don't forget, there is more than forgetting: new metrics for Continual Learning	Oct 31, 2018	AttributeComputational Efficiency	—Unverified
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency	Nov 9, 2023	Deep LearningFederated Learning	—Unverified
Don't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification	Oct 30, 2024	DecoderLearning-To-Rank	—Unverified
Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding	Oct 8, 2020	Cross-Lingual TransferNatural Language Understanding	—Unverified
Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning	Jan 24, 2024	Transfer Learning	—Unverified
Don’t throw away that linear head: Few-shot protein fitness prediction with generative models	Sep 29, 2021	Transfer Learning	—Unverified
Automatic segmentation of texts into units of meaning for reading assistance	Oct 11, 2019	Cultural Vocal Bursts Intensity PredictionTransfer Learning	—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