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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9101–9125 of 10307 papers

TitleStatusHype
Class dependency based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of Tuberculosis from chest x-rays—0
Classical-to-quantum convolutional neural network transfer learning—0
Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks—0
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks—0
Classification Algorithm of Speech Data of Parkinsons Disease Based on Convolution Sparse Kernel Transfer Learning with Optimal Kernel and Parallel Sample Feature Selection—0
Classification of Alzheimer's Disease Using the Convolutional Neural Network (CNN) with Transfer Learning and Weighted Loss—0
Brain informed transfer learning for categorizing construction hazards—0
Quantum median filter for Total Variation image denoising—0
Classification Of Automotive Targets Using Inverse Synthetic Aperture Radar Images—0
Classification of Beer Bottles using Object Detection and Transfer Learning—0
Classification of All Blood Cell Images using ML and DL Models—0
Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes—0
Classification of breast cancer histology images using transfer learning—0
Classification of Breast Cancer Lesions in Ultrasound Images by using Attention Layer and loss Ensembles in Deep Convolutional Neural Networks—0
Quantum Transfer Learning for Acceptability Judgements—0
Classification of Chest Diseases using Wavelet Transforms and Transfer Learning—0
Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing—0
Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines—0
Quantum Transfer Learning for MNIST Classification Using a Hybrid Quantum-Classical Approach—0
Quantum Transfer Learning for Wi-Fi Sensing—0
Classification of COVID-19 Patients with their Severity Level from Chest CT Scans using Transfer Learning—0
Classification of Diabetic Retinopathy Using Unlabeled Data and Knowledge Distillation—0
Classification of Diabetic Retinopathy via Fundus Photography: Utilization of Deep Learning Approaches to Speed up Disease Detection—0
Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher—0
Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models—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