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 4281–4290 of 10307 papers

Title	Date	Tasks	Status	Hype
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights	Sep 29, 2022	Knowledge DistillationNeural Architecture Search	CodeCode Available	1
Facial Landmark Predictions with Applications to Metaverse	Sep 29, 2022	Decodertext-to-speech	CodeCode Available	0
Bidirectional Language Models Are Also Few-shot Learners	Sep 29, 2022	DenoisingLanguage Modeling	—Unverified	0
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention	Sep 28, 2022	Training-free 3D Point Cloud ClassificationTransfer Learning	CodeCode Available	1
Learning Deep Representations via Contrastive Learning for Instance Retrieval	Sep 28, 2022	Contrastive LearningImage Retrieval	—Unverified	0
Data Augmentation using Feature Generation for Volumetric Medical Images	Sep 28, 2022	ClassificationData Augmentation	—Unverified	0
Cyclegan Network for Sheet Metal Welding Drawing Translation	Sep 28, 2022	Machine TranslationSSIM	—Unverified	0
Transfer Learning with Pretrained Remote Sensing Transformers	Sep 28, 2022	Transfer Learning	CodeCode Available	1
Design Perspectives of Multitask Deep Learning Models and Applications	Sep 27, 2022	Deep LearningMulti-Task Learning	—Unverified	0
Regularized Soft Actor-Critic for Behavior Transfer Learning	Sep 27, 2022	continuous-controlContinuous Control	—Unverified	0

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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