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 6326–6350 of 10307 papers

TitleStatusHype
A Computer Vision Approach to Combat Lyme Disease—0
Improvement of Applicability in Student Performance Prediction Based on Transfer Learning—0
Improvements to context based self-supervised learning—0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding—0
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images—0
Semantically Proportional Patchmix for Few-Shot Learning—0
A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors—0
Improving Accuracy of Nonparametric Transfer Learning via Vector Segmentation—0
Semantic and Visual Similarities for Efficient Knowledge Transfer in CNN Training—0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study—0
Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning—0
A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images—0
Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation—0
Improving BERT with Hybrid Pooling Network and Drop Mask—0
Improving Bias in Facial Attribute Classification: A Combined Impact of KL Divergence induced Loss Function and Dual Attention—0
Improving Botnet Detection with Recurrent Neural Network and Transfer Learning—0
Improving Buoy Detection with Deep Transfer Learning for Mussel Farm Automation—0
Ten Challenging Problems in Federated Foundation Models—0
A Primer on Pretrained Multilingual Language Models—0
Improving Cause-of-Death Classification from Verbal Autopsy Reports—0
Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning—0
Improving Classification through Weak Supervision in Context-specific Conversational Agent Development for Teacher Education—0
Improving Code Autocompletion with Transfer Learning—0
Improving Commonsense Contingent Reasoning by Pseudo-data and Its Application to the Related Tasks—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