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 1161–1170 of 10307 papers

Title	Date	Tasks	Status	Hype	Score
Exploring Transfer Learning for Low Resource Emotional TTS	Jan 14, 2019	Deep LearningEmotional Speech Synthesis	CodeCode Available	1	5
A Unified Framework for Domain Adaptive Pose Estimation	Apr 1, 2022	2D Pose EstimationAnimal Pose Estimation	CodeCode Available	1	5
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition	Oct 8, 2021	Spoken Command RecognitionTransfer Learning	CodeCode Available	1	5
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles	Mar 25, 2021	Domain AdaptationGeometric Matching	CodeCode Available	1	5
HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models	Mar 28, 2023	DecoderHuman-Object Interaction Detection	CodeCode Available	1	5
How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs	Aug 1, 2018	Multi-Task LearningTransfer Learning	CodeCode Available	1	5
Chip Placement with Deep Reinforcement Learning	Apr 22, 2020	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
A benchmark dataset for deep learning-based airplane detection: HRPlanes	Apr 22, 2022	Deep Learningobject-detection	CodeCode Available	1	5
A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learning	Mar 5, 2024	Contrastive LearningTransfer Learning	CodeCode Available	1	5
A Study of Face Obfuscation in ImageNet	Mar 10, 2021	AttributeObject	CodeCode Available	1	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