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 3126–3150 of 10307 papers

TitleStatusHype
Domain Adaptation Broad Learning System Based on Locally Linear Embedding—0
Driver Safety Development Real Time Driver Drowsiness Detection System Based on Convolutional Neural Network—0
Domain Adaptation by Topology Regularization—0
Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts—0
Domain adaptation for holistic skin detection—0
Automatic Tuberculosis and COVID-19 cough classification using deep learning—0
Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts—0
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks—0
Domain Adaptation for Reinforcement Learning on the Atari—0
Deep Learning-Based Communication Over the Air—0
Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning—0
Domain Adaptation for Visual Applications: A Comprehensive Survey—0
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
Domain Adaptation for Robot Predictive Maintenance Systems—0
Domain adaptation in small-scale and heterogeneous biological datasets—0
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer—0
Domain Adaptation of low-resource Target-Domain models using well-trained ASR Conformer Models—0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey—0
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers—0
Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data—0
Domain Adaptation using Silver Standard Masks for Lateral Ventricle Segmentation in FLAIR MRI—0
Deep Learning-based Bio-Medical Image Segmentation using UNet Architecture and Transfer Learning—0
An Information-Theoretic Approach to Transferability in Task Transfer Learning—0
Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition—0
Deep Learning based Automated Forest Health Diagnosis from Aerial Images—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