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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4276–4300 of 10307 papers

TitleStatusHype
Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method—0
A Physics-preserved Transfer Learning Method for Differential Equations—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware—0
End-to-end acoustic modelling for phone recognition of young readers—0
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning—0
End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving—0
Encoding Explanatory Knowledge for Zero-shot Science Question Answering—0
Meta-models for transfer learning in source localisation—0
Char-RNN for Word Stress Detection in East Slavic Languages—0
A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations—0
Action Recognition using Transfer Learning and Majority Voting for CSGO—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming—0
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm—0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Enabling Low-Resource Transfer Learning across COVID-19 Corpora by Combining Event-Extraction and Co-Training—0
Characterizing and Avoiding Negative Transfer—0
Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision—0
Enabling Incremental Knowledge Transfer for Object Detection at the Edge—0
Enabling hand gesture customization on wrist-worn devices—0
Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer—0
The (In)Effectiveness of Intermediate Task Training For Domain Adaptation and Cross-Lingual Transfer Learning—0
A physics-based domain adaptation framework for modelling and forecasting building energy systems—0
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments—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