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 6551–6575 of 10307 papers

TitleStatusHype
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction—0
Semi-supervised Object Detection: A Survey on Recent Research and Progress—0
Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study—0
Interpretation of Chest x-rays affected by bullets using deep transfer learning—0
Semi-supervised Regression Analysis with Model Misspecification and High-dimensional Data—0
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages—0
Semi-Supervising Learning, Transfer Learning, and Knowledge Distillation with SimCLR—0
Redundancy and Concept Analysis for Code-trained Language Models—0
Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning—0
Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding—0
Inter-Subject Variance Transfer Learning for EMG Pattern Classification Based on Bayesian Inference—0
Testing the Generalization Power of Neural Network Models Across NLI Benchmarks—0
Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features?—0
Intra-Domain Task-Adaptive Transfer Learning to Determine Acute Ischemic Stroke Onset Time—0
A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations—0
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals—0
Robustness via Deep Low-Rank Representations—0
Sense and Learn: Self-Supervision for Omnipresent Sensors—0
A physics-based domain adaptation framework for modelling and forecasting building energy systems—0
Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets—0
Introducing the structural bases of typicality effects in deep learning—0
Introspective Action Advising for Interpretable Transfer Learning—0
Your representations are in the network: composable and parallel adaptation for large scale models—0
Sense representations for Portuguese: experiments with sense embeddings and deep neural language models—0
Inverse Density as an Inverse Problem: The Fredholm Equation Approach—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