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 8101–8125 of 10307 papers

TitleStatusHype
A Hybrid Approach of Transfer Learning and Physics-Informed Modeling: Improving Dissolved Oxygen Concentration Prediction in an Industrial Wastewater Treatment Plant—0
SSMT: Few-Shot Traffic Forecasting with Single Source Meta-Transfer—0
Neural Collapse: A Review on Modelling Principles and Generalization—0
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning—0
Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data—0
SSN@LT-EDI-ACL2022: Transfer Learning using BERT for Detecting Signs of Depression from Social Media Texts—0
SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD Detection from Visual Attention Tasks—0
A Hybrid Approach for COVID-19 Detection: Combining Wasserstein GAN with Transfer Learning—0
Neural Entity Linking on Technical Service Tickets—0
A HMAX with LLC for visual recognition—0
A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data—0
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?—0
The Effects of Input Type and Pronunciation Dictionary Usage in Transfer Learning for Low-Resource Text-to-Speech—0
Neural Machine Translation in Low-Resource Setting: a Case Study in English-Marathi Pair—0
The elements of flexibility for task-performing systems—0
Neural Network based Deep Transfer Learning for Cross-domain Dependency Parsing—0
Neural Networks can Learn Representations with Gradient Descent—0
Stable Diffusion Dataset Generation for Downstream Classification Tasks—0
Neural Networks Trained to Solve Differential Equations Learn General Representations—0
Neural Network Training Using _1-Regularization and Bi-fidelity Data—0
Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning—0
Neural Paraphrase Generation using Transfer Learning—0
Neural Population Learning beyond Symmetric Zero-sum Games—0
A Hierarchical Neural Framework for Classification and its Explanation in Large Unstructured Legal Documents—0
Neural Program Meta-Induction—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