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 4191–4200 of 10307 papers

Title	Date	Tasks	Status	Hype
Text-Derived Knowledge Helps Vision: A Simple Cross-modal Distillation for Video-based Action Anticipation	Oct 12, 2022	Action AnticipationTransfer Learning	CodeCode Available	0
Token-Label Alignment for Vision Transformers	Oct 12, 2022	image-classificationImage Classification	CodeCode Available	1
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding	Oct 12, 2022	Multi-Task LearningTransfer Learning	CodeCode Available	0
Investigating Massive Multilingual Pre-Trained Machine Translation Models for Clinical Domain via Transfer Learning	Oct 12, 2022	Machine TranslationNMT	—Unverified	0
Prompt Generation Networks for Input-Space Adaptation of Frozen Vision Transformers	Oct 12, 2022	Prompt LearningTransfer Learning	CodeCode Available	1
Self-supervised video pretraining yields robust and more human-aligned visual representations	Oct 12, 2022	Contrastive Learningobject-detection	—Unverified	0
CLIP also Understands Text: Prompting CLIP for Phrase Understanding	Oct 11, 2022	ClusteringTransfer Learning	—Unverified	0
The Role of Exploration for Task Transfer in Reinforcement Learning	Oct 11, 2022	reinforcement-learningReinforcement Learning	—Unverified	0
Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment Analysis	Oct 11, 2022	Multimodal Sentiment AnalysisSentiment Analysis	CodeCode Available	1
Combining datasets to increase the number of samples and improve model fitting	Oct 11, 2022	ImputationTime Series Analysis	CodeCode Available	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