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 2071–2080 of 10307 papers

Title	Date	Tasks	Status	Score
GIST at SemEval-2018 Task 12: A network transferring inference knowledge to Argument Reasoning Comprehension task	Jun 1, 2018	Common Sense ReasoningNatural Language Inference	CodeCode Available	5
Getting aligned on representational alignment	Oct 18, 2023	NavigateTransfer Learning	CodeCode Available	5
Geostatistical Learning: Challenges and Opportunities	Feb 17, 2021	Learning TheoryModel Selection	CodeCode Available	5
GERNERMED++: Transfer Learning in German Medical NLP	Jun 29, 2022	Machine Translationnamed-entity-recognition	CodeCode Available	5
GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment	May 30, 2024	GSM8KKnowledge Distillation	CodeCode Available	5
Boosting High Resolution Image Classification with Scaling-up Transformers	Sep 26, 2023	ClassificationData Augmentation	CodeCode Available	5
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation	May 17, 2022	Low-Resource Neural Machine TranslationMachine Translation	CodeCode Available	5
Boosting Handwriting Text Recognition in Small Databases with Transfer Learning	Apr 4, 2018	HTRTransfer Learning	CodeCode Available	5
A Diffusion Model and Knowledge Distillation Framework for Robust Coral Detection in Complex Underwater Environments	Jan 6, 2025	2D Object DetectionKnowledge Distillation	CodeCode Available	5
Generative Transfer Learning: Covid-19 Classification with a few Chest X-ray Images	Aug 10, 2022	Binary ClassificationFew-Shot 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