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 481–490 of 10307 papers

Title	Date	Tasks	Status	Hype
Efficient Conditional GAN Transfer with Knowledge Propagation across Classes	Feb 12, 2021	Conditional Image GenerationImage Generation	CodeCode Available	1
Efficient Crowd Counting via Structured Knowledge Transfer	Mar 23, 2020	Crowd CountingGPU	CodeCode Available	1
A Whisper transformer for audio captioning trained with synthetic captions and transfer learning	May 15, 2023	Audio captioningSpeech-to-Text	CodeCode Available	1
Bag of Tricks for Image Classification with Convolutional Neural Networks	Dec 4, 2018	Domain GeneralizationGeneral Classification	CodeCode Available	1
AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning	Sep 3, 2020	BIG-bench Machine LearningTransfer Learning	CodeCode Available	1
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts	Feb 3, 2018	Active LearningTransfer Learning	CodeCode Available	1
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement	Sep 20, 2021	Image-level Supervised Instance SegmentationInstance Segmentation	CodeCode Available	1
Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters	Aug 3, 2021	Objectobject-detection	CodeCode Available	1
A fuzzy distance-based ensemble of deep models for cervical cancer detection	Mar 30, 2022	Image ClassificationTransfer Learning	CodeCode Available	1
Chip Placement with Deep Reinforcement Learning	Apr 22, 2020	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1

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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