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 1201–1210 of 10307 papers

Title	Date	Tasks	Status	Hype	Score
COLA: Cross-city Mobility Transformer for Human Trajectory Simulation	Mar 4, 2024	CoLATransfer Learning	CodeCode Available	1	5
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference	Jun 7, 2021	Cross-Lingual TransferNatural Language Inference	CodeCode Available	1	5
New Protocols and Negative Results for Textual Entailment Data Collection	Apr 24, 2020	BenchmarkingDiversity	CodeCode Available	1	5
Audio-based Near-Duplicate Video Retrieval with Audio Similarity Learning	Oct 17, 2020	RetrievalTransfer Learning	CodeCode Available	1	5
Audio Embeddings as Teachers for Music Classification	Jun 30, 2023	ClassificationInformation Retrieval	CodeCode Available	1	5
Audio Spoofing Verification using Deep Convolutional Neural Networks by Transfer Learning	Aug 8, 2020	Speaker VerificationTransfer Learning	CodeCode Available	1	5
A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles	Feb 22, 2022	Deep Reinforcement Learningenergy management	CodeCode Available	1	5
Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data	Apr 11, 2022	Inference AttackMembership Inference Attack	CodeCode Available	1	5
Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion Recognition	Aug 15, 2020	Emotion RecognitionMultimodal Deep Learning	CodeCode Available	1	5
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning	Feb 13, 2023	Deep Reinforcement Learningreinforcement-learning	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