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 3411–3420 of 10307 papers

Title	Date	Tasks	Status	Score
Empower Sequence Labeling with Task-Aware Neural Language Model	Sep 13, 2017	GPULanguage Modeling	CodeCode Available	5
Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting	Dec 18, 2024	GSM8KKnowledge Distillation	CodeCode Available	5
Content-Based Landmark Retrieval Combining Global and Local Features using Siamese Neural Networks	Aug 3, 2022	Metric LearningRe-Ranking	CodeCode Available	5
eGAN: Unsupervised approach to class imbalance using transfer learning	Apr 9, 2021	BIG-bench Machine LearningClassification	CodeCode Available	5
Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R	Mar 16, 2024	Code Summarizationparameter-efficient fine-tuning	CodeCode Available	5
Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery	Dec 19, 2023	Representation LearningTransfer Learning	CodeCode Available	5
Emoji-Based Transfer Learning for Sentiment Tasks	Feb 12, 2021	Hate Speech DetectionSentiment Analysis	CodeCode Available	5
Elastic Coupled Co-clustering for Single-Cell Genomic Data	Mar 29, 2020	ClusteringData Integration	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
Constrained Decoding for Cross-lingual Label Projection	Feb 5, 2024	Cross-Lingual TransferEvent Argument Extraction	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