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 8626–8650 of 10307 papers

TitleStatusHype
Walking the Tightrope: An Investigation of the Convolutional Autoencoder BottleneckCode0
Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store ReviewsCode0
Warm Starting CMA-ES for Hyperparameter OptimizationCode0
Towards a text-based quantitative and explainable histopathology image analysisCode0
Towards Speaker Identification with Minimal Dataset and Constrained Resources using 1D-Convolution Neural NetworkCode0
Transfer Learning for Low-Resource Sentiment AnalysisCode0
Z-upscaling: Optical Flow Guided Frame Interpolation for Isotropic Reconstruction of 3D EM VolumesCode0
When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?Code0
Unified Object Detector for Different Modalities based on Vision TransformersCode0
Transfer Fine-Tuning: A BERT Case StudyCode0
Transfer Learning for Low-Resource Neural Machine TranslationCode0
Transfer Learning for Improving Speech Emotion Classification AccuracyCode0
When does Parameter-Efficient Transfer Learning Work for Machine Translation?Code0
Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principlesCode0
Transfer Learning for Improving Results on Russian Sentiment DatasetsCode0
Transfer Learning for Image-Based Malware ClassificationCode0
UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code GenerationCode0
Transfer Learning for Illustration ClassificationCode0
Transfer Learning for HVAC System Fault DetectionCode0
Transfer Learning for Health-related Twitter DataCode0
Transfer Learning for Entity Recognition of Novel ClassesCode0
When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood PerspectiveCode0
Transfer Learning for Electricity Price ForecastingCode0
Transfer Learning for Deep Learning-based Prediction of Lattice Thermal ConductivityCode0
Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time seriesCode0
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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