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 7801–7825 of 10307 papers

TitleStatusHype
Multidomain Document Layout Understanding using Few Shot Object Detection—0
Multi-Domain Evolutionary Optimization of Network Structures—0
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs—0
Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition—0
A Machine Learning Approach for Predicting Human Preference for Graph Layouts—0
Multi-Domain Sentiment Relevance Classification with Automatic Representation Learning—0
Multi-Environment based Meta-Learning with CSI Fingerprints for Radio Based Positioning—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis—0
Multi-Fidelity Bayesian Neural Network for Uncertainty Quantification in Transonic Aerodynamic Loads—0
Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images—0
Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data—0
Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator—0
Multi-fidelity reinforcement learning framework for shape optimization—0
Multifidelity Simulation-based Inference for Computationally Expensive Simulators—0
ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts—0
Spatial Transfer Learning with Simple MLP—0
All-Transfer Learning for Deep Neural Networks and its Application to Sepsis Classification—0
Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks—0
Spatial Transformer Network with Transfer Learning for Small-scale Fine-grained Skeleton-based Tai Chi Action Recognition—0
All-in-One: Transferring Vision Foundation Models into Stereo Matching—0
Multi-kernel Passive Stochastic Gradient Algorithms and Transfer Learning—0
Multi-Label and Multilingual News Framing Analysis—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