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 7901–7925 of 10307 papers

TitleStatusHype
Learning to Rank Learning Curves—0
Filtered Inner Product Projection for Crosslingual Embedding Alignment—0
Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment—0
Continuous Transfer Learning with Label-informed Distribution Alignment—0
Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning—0
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge—0
A Siamese Neural Network with Modified Distance Loss For Transfer Learning in Speech Emotion Recognition—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning—0
Refined Continuous Control of DDPG Actors via Parametrised Activation—0
Fast CRDNN: Towards on Site Training of Mobile Construction Machines—0
Neuroevolutionary Transfer Learning of Deep Recurrent Neural Networks through Network-Aware Adaptation—0
Learning across label confidence distributions using Filtered Transfer Learning—0
Meta Dialogue Policy Learning—0
Transfer Learning for British Sign Language Modelling—0
WikiBERT models: deep transfer learning for many languagesCode0
Deep Learning of Dynamic Subsurface Flow via Theory-guided Generative Adversarial Network—0
Learning to Branch for Multi-Task Learning—0
A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems—0
Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methodsCode0
Reconnaissance de phones fond\'ee sur du Transfer Learning pour des enfants apprenants lecteurs en environnement de classe (Transfer Learning based phone recognition on children learning to read, with speech recorded in a classroom environment)—0
A Systematic Study of Inner-Attention-Based Sentence Representations in Multilingual Neural Machine Translation—0
Similarit\'e s\'emantique entre phrases : apprentissage par transfert interlingue (Semantic Sentence Similarity : Multilingual Transfer Learning)—0
Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving—0
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes—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