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 4201–4225 of 10307 papers

TitleStatusHype
Autism Spectrum Disorder Classification in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder—0
Transfer learning for semantic similarity measures based on symbolic regressionCode0
Shared Growth of Graph Neural Networks via Prompted Free-direction Knowledge Distillation—0
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model—0
Unified Transfer Learning Models in High-Dimensional Linear Regression—0
Towards the extraction of robust sign embeddings for low resource sign language recognition—0
Improving the Transferability of Time Series Forecasting with Decomposition Adaptation—0
Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundariesCode0
Scalable method for Bayesian experimental design without integrating over posterior distributionCode0
Sampling weights of deep neural networksCode0
Obeying the Order: Introducing Ordered Transfer Hyperparameter OptimisationCode0
Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall ClassificationCode0
Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning—0
Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures—0
Multi-Scenario Ranking with Adaptive Feature Learning—0
Recent Advances in Optimal Transport for Machine Learning—0
A serial dual-channel library occupancy detection system based on Faster RCNN—0
Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical PruningCode0
Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift—0
Transfer Learning with Random Coefficient Ridge Regression—0
Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods—0
Theater Aid System for the Visually Impaired Through Transfer Learning of Spatio-Temporal Graph Convolution Networks—0
Transferability Metrics for Object DetectionCode0
Approximated Prompt Tuning for Vision-Language Pre-trained Models—0
CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective Models on French Biomedical Data—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