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 9051–9075 of 10307 papers

TitleStatusHype
A Unified Neural Architecture for Instrumental Audio TasksCode0
Meta Dynamic Pricing: Transfer Learning Across Experiments—0
Learning Representations from Persian Handwriting for Offline Signature Verification, a Deep Transfer Learning Approach—0
Machine-assisted annotation of forensic imagery—0
Active Transfer Learning for Persian Offline Signature Verification—0
FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer LearningCode0
Robust Authorship Verification with Transfer Learning—0
DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence FusionCode0
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language ModelsCode0
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular DataCode0
Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural NetworksCode0
Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?—0
Transfer Learning for Performance Modeling of Configurable Systems: A Causal AnalysisCode0
Transfer Learning for Sequences via Learning to Collocate—0
Bengali Handwritten Character Classification using Transfer Learning on Deep Convolutional Neural NetworkCode0
Medical Multimodal Classifiers Under Scarce Data Condition—0
Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract—0
Transfer Learning for Non-Intrusive Load MonitoringCode0
A visual encoding model based on deep neural networks and transfer learning—0
Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts—0
A new Potential-Based Reward Shaping for Reinforcement Learning Agent—0
LapEPI-Net: A Laplacian Pyramid EPI structure for Learning-based Dense Light Field Reconstruction—0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media—0
Transfusion: Understanding Transfer Learning for Medical ImagingCode0
Transfer Learning for Sequence Labeling Using Source Model and Target 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