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 3431–3440 of 10307 papers

Title	Date	Tasks	Status	Score
Content-Based Landmark Retrieval Combining Global and Local Features using Siamese Neural Networks	Aug 3, 2022	Metric LearningRe-Ranking	CodeCode Available	5
A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment	Feb 9, 2022	Transfer LearningVideo Quality Assessment	CodeCode Available	5
Embedding neurophysiological signals	Oct 25, 2022	Brain Computer InterfaceDomain Adaptation	CodeCode Available	5
Embedding Ordinality to Binary Loss Function for Improving Solar Flare Forecasting	Aug 21, 2024	Solar Flare PredictionTransfer Learning	CodeCode Available	5
Encodings for Prediction-based Neural Architecture Search	Mar 4, 2024	Neural Architecture SearchPrediction	CodeCode Available	5
End-to-End Deep Learning of Optimization Heuristics	Nov 2, 2017	Deep LearningGPU	CodeCode Available	5
Empower Sequence Labeling with Task-Aware Neural Language Model	Sep 13, 2017	GPULanguage Modeling	CodeCode Available	5
Topological Learning for Motion Data via Mixed Coordinates	Oct 30, 2023	Time SeriesTransfer Learning	CodeCode Available	5
Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing	Aug 28, 2024	Edge-computingOne-Shot Learning	CodeCode Available	5
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning	Dec 1, 2018	Few-Shot LearningMetric Learning	CodeCode Available	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