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 1776–1800 of 10307 papers

TitleStatusHype
Application of DenseNet in Camera Model Identification and Post-processing Detection—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
Continuous Transfer Learning for UAV Communication-aware Trajectory Design—0
Applying Transfer Learning for Improving Domain-Specific Search Experience Using Query to Question Similarity—0
Classification Of Automotive Targets Using Inverse Synthetic Aperture Radar Images—0
Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol—0
Classification of All Blood Cell Images using ML and DL Models—0
Approaches for enhancing extrapolability in process-based and data-driven models in hydrology—0
Classification of breast cancer histology images using transfer learning—0
Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation—0
Approaching Neural Chinese Word Segmentation as a Low-Resource Machine Translation Task—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
Application of Artificial Intelligence in the Classification of Microscopical Starch Images for Drug Formulation—0
CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition—0
Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People—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
Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network—0
Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models—0
Classification of Geographical Land Structure Using Convolution Neural Network and Transfer Learning—0
Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor—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