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 91–100 of 10307 papers

Title	Date	Tasks	Status	Hype	Score
Feature Learning in Infinite-Width Neural Networks	Nov 30, 2020	Few-Shot LearningTransfer Learning	CodeCode Available	2	5
3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation	Sep 29, 2022	Image SegmentationMedical Image Segmentation	CodeCode Available	2	5
All-in-one foundational models learning across quantum chemical levels	Sep 18, 2024	AllCloud Computing	CodeCode Available	2	5
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning	Nov 22, 2021	DenoisingMulti-Task Learning	CodeCode Available	2	5
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning	Mar 23, 2024	Federated LearningTransfer Learning	CodeCode Available	2	5
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation	Jul 27, 2020	Face GenerationTransfer Learning	CodeCode Available	2	5
Content-Based Search for Deep Generative Models	Oct 6, 2022	Contrastive LearningImage and Sketch based Model Retrieval	CodeCode Available	2	5
Continual Pre-training of Language Models	Feb 7, 2023	Continual LearningContinual Pretraining	CodeCode Available	2	5
AdapterFusion: Non-Destructive Task Composition for Transfer Learning	May 1, 2020	Language ModellingMulti-Task Learning	CodeCode Available	2	5
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images	Oct 25, 2023	Transfer Learning	CodeCode Available	2	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