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 9751–9775 of 10307 papers

TitleStatusHype
Transfer Learning with Binary Neural Networks—0
Towards Alzheimer's Disease Classification through Transfer LearningCode0
Deep Learning for identifying radiogenomic associations in breast cancer—0
Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay—0
Modeling Information Flow Through Deep Neural Networks—0
Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network—0
Learning to Rank based on Analogical Reasoning—0
Exposing Computer Generated Images by Using Deep Convolutional Neural Networks—0
Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks—0
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ—0
Learning to cluster in order to transfer across domains and tasksCode0
Transfer Learning in CNNs Using Filter-Trees—0
Smartphone App Usage Prediction Using Points of Interest—0
Expectation maximization transfer learning and its application for bionic hand prostheses—0
Adversarial Transfer Learning for Cross-domain Visual Recognition—0
Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic ImageryCode0
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce—0
10Sent: A Stable Sentiment Analysis Method Based on the Combination of Off-The-Shelf Approaches—0
Domain Generalization by Marginal Transfer LearningCode0
Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity RecognitionCode0
Glitch Classification and Clustering for LIGO with Deep Transfer Learning—0
Optical Character Recognition (OCR) for Telugu: Database, Algorithm and Application—0
Zero-shot Learning via Shared-Reconstruction-Graph Pursuit—0
Vision Based Railway Track Monitoring using Deep Learning—0
Improvements to context based self-supervised learning—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