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 3501–3510 of 10307 papers

Title	Date	Tasks	Status
Deep Discriminative Fine-Tuning for Cancer Type Classification	Nov 15, 2019	Cancer type classificationClassification	—Unverified
Automatic detection of passable roads after floods in remote sensed and social media data	Jan 10, 2019	BenchmarkingTransfer Learning	—Unverified
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020	Jun 4, 2020	Data AugmentationKnowledge Distillation	—Unverified
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model	Apr 29, 2022	4kConversational Question Answering	—Unverified
End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning	Apr 13, 2019	Cross-Lingual Transfertext-to-speech	—Unverified
End-to-end transfer learning for speaker-independent cross-language and cross-corpus speech emotion recognition	Nov 22, 2023	Cross-corpusEmotion Recognition	—Unverified
Deep Decomposition for Stochastic Normal-Abnormal Transport	Nov 29, 2021	Optical Flow EstimationTime Series	—Unverified
End-to-end Whispered Speech Recognition with Frequency-weighted Approaches and Pseudo Whisper Pre-training	May 5, 2020	speech-recognitionSpeech Recognition	—Unverified
Energy Clustering for Unsupervised Person Re-identification	Aug 31, 2019	ClusteringDiversity	—Unverified
Automatic detection of rare pathologies in fundus photographs using few-shot learning	Jul 22, 2019	Few-Shot LearningOne-Shot Learning	—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