SOTAVerified

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 8426–8450 of 10307 papers

TitleStatusHype
Optical Character Recognition using Convolutional Neural Networks for Ashokan Brahmi Inscriptions—0
Optimal Bayesian Transfer Learning—0
Optimal Layer Selection for Latent Data Augmentation—0
Optimal Policy Adaptation under Covariate Shift—0
The HIT-SCIR System for End-to-End Parsing of Universal Dependencies—0
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion—0
Optimal Transfer Learning Model for Binary Classification of Funduscopic Images through Simple Heuristics—0
Optimal transfer protocol by incremental layer defrosting—0
Optimal Transport for Deep Joint Transfer Learning—0
Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications—0
Optimising the Performance of Convolutional Neural Networks across Computing Systems using Transfer Learning—0
Student Activity Recognition in Classroom Environments using Transfer Learning—0
Optimization of Transfer Learning for Sign Language Recognition Targeting Mobile Platform—0
Student Network Learning via Evolutionary Knowledge Distillation—0
Optimized Machine Learning for CHD Detection using 3D CNN-based Segmentation, Transfer Learning and Adagrad Optimization—0
Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning—0
Optimizing Active Learning for Low Annotation Budgets—0
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian—0
Student-Oriented Teacher Knowledge Refinement for Knowledge Distillation—0
Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification—0
Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off—0
Optimizing Dense Feed-Forward Neural Networks—0
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels—0
Optimizing Knowledge Distillation in Transformers: Enabling Multi-Head Attention without Alignment Barriers—0
Student/Teacher Advising through Reward Augmentation—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified