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 8576–8600 of 10307 papers

TitleStatusHype
Zero-Shot Transfer Learning for Event ExtractionCode0
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational dataCode0
Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine InterfacesCode0
Timage -- A Robust Time Series Classification PipelineCode0
Towards Deep Cellular Phenotyping in Placental HistologyCode0
Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote SensingCode0
Improved object recognition using neural networks trained to mimic the brain's statistical propertiesCode0
Transfer Learning for OCRopus Model Training on Early Printed BooksCode0
Training neural audio classifiers with few dataCode0
Uncertainty Quantified Deep Learning and Regression Analysis Framework for Image Segmentation of Skin Cancer LesionsCode0
VLN-PETL: Parameter-Efficient Transfer Learning for Vision-and-Language NavigationCode0
Training Deep Networks from Zero to Hero: avoiding pitfalls and going beyondCode0
Topological Learning for Motion Data via Mixed CoordinatesCode0
Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart HomesCode0
Towards Compact ConvNets via Structure-Sparsity Regularized Filter PruningCode0
Using Machine Learning to Determine Morphologies of z<1 AGN Host Galaxies in the Hyper Suprime-Cam Wide SurveyCode0
Transfer Learning for Nonparametric Contextual Dynamic PricingCode0
World Model Agents with Change-Based Intrinsic MotivationCode0
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasmCode0
Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASRCode0
Using Mobility for Electrical Load Forecasting During the COVID-19 PandemicCode0
Worst-Case-Aware Curriculum Learning for Zero and Few Shot TransferCode0
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer NetworksCode0
Understanding Deep Representation Learning via Layerwise Feature Compression and DiscriminationCode0
Transfer Learning for Non-Intrusive Load MonitoringCode0
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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