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

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Showing 3671–3680 of 10307 papers

Title	Date	Tasks	Status
Boosting Convolutional Neural Networks' Protein Binding Site Prediction Capacity Using SE(3)-invariant transformers, Transfer Learning and Homology-based Augmentation	Feb 20, 2023	Drug DiscoveryTransfer Learning	—Unverified
Exploring the Limits of Transfer Learning with Unified Model in the Cybersecurity Domain	Feb 20, 2023	ArticlesTransfer Learning	—Unverified
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness	Feb 20, 2023	Neural Architecture SearchTransfer Learning	—Unverified
Few-shot Multimodal Multitask Multilingual Learning	Feb 19, 2023	Few-Shot LearningIn-Context Learning	—Unverified
Why Is Public Pretraining Necessary for Private Model Training?	Feb 19, 2023	Transfer Learning	—Unverified
Multilingual Content Moderation: A Case Study on Reddit	Feb 19, 2023	Cross-Lingual TransferTransfer Learning	CodeCode Available
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers	Feb 19, 2023	Knowledge DistillationModel Compression	—Unverified
A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasets	Feb 19, 2023	Deep LearningTransfer Learning	—Unverified
Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences	Feb 17, 2023	Multi-Object TrackingObject	—Unverified
Adversarial Contrastive Distillation with Adaptive Denoising	Feb 17, 2023	Adversarial RobustnessDenoising	—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