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

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Showing 1901–1910 of 10307 papers

Title	Date	Tasks	Status	Score
HANA: A HAndwritten NAme Database for Offline Handwritten Text Recognition	Jan 22, 2021	Handwritten Text RecognitionTransfer Learning	CodeCode Available	5
Are we done with object recognition? The iCub robot's perspective	Sep 28, 2017	Deep LearningImage Retrieval	CodeCode Available	5
Learning to Collaborate Over Graphs: A Selective Federated Multi-Task Learning Approach	Jun 11, 2025	Community DetectionFairness	CodeCode Available	5
Collaborative Deep Reinforcement Learning	Feb 19, 2017	Deep Reinforcement LearningKnowledge Distillation	CodeCode Available	5
Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publications	Nov 14, 2024	Information RetrievalRAG	CodeCode Available	5
Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence Function	Sep 14, 2022	Decision MakingDeep Reinforcement Learning	CodeCode Available	5
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language Models	Dec 7, 2023	Multi-Task LearningPassage Retrieval	CodeCode Available	5
Can a powerful neural network be a teacher for a weaker neural network?	May 1, 2020	Transfer Learning	CodeCode Available	5
Hacking Task Confounder in Meta-Learning	Dec 10, 2023	Meta-LearningTransfer Learning	CodeCode Available	5
Advancing Adversarial Suffix Transfer Learning on Aligned Large Language Models	Aug 27, 2024	Red TeamingTransfer 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