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 7876–7900 of 10307 papers

TitleStatusHype
Unsupervised Word Translation Pairing using Refinement based Point Set Registration—0
Unsupervised Word Translation with Adversarial Autoencoder—0
Untangling the Influence of Typology, Data and Model Architecture on Ranking Transfer Languages for Cross-Lingual POS Tagging—0
Untargeted Code Authorship Evasion with Seq2Seq Transformation—0
Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions—0
UoB\_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.—0
UofA-Truth at Factify 2022 : Transformer And Transfer Learning Based Multi-Modal Fact-Checking—0
UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification—0
UParse: the Edinburgh system for the CoNLL 2017 UD shared task—0
Urban flows prediction from spatial-temporal data using machine learning: A survey—0
UrbanSARFloods: Sentinel-1 SLC-Based Benchmark Dataset for Urban and Open-Area Flood Mapping—0
Urban traffic analysis and forecasting through shared Koopman eigenmodes—0
Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation—0
Use of Deep Neural Networks for Uncertain Stress Functions with Extensions to Impact Mechanics—0
Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models—0
Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection—0
User lung cancer classification using efficientnet from ct scan images—0
User-specific Adaptive Fine-tuning for Cross-domain Recommendations—0
Using Artificial Intelligence for the Automation of Knitting Patterns—0
Using autoencoders and deep transfer learning to determine the stellar parameters of 286 CARMENES M dwarfs—0
Using Deep Networks and Transfer Learning to Address Disinformation—0
Towards Using Diachronic Distributed Word Representations as Models of Lexical Development—0
Using different sources of ground truths and transfer learning to improve the generalization of photometric redshift estimation—0
Using Domain Knowledge for Low Resource Named Entity Recognition—0
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection—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