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 6951–6975 of 10307 papers

TitleStatusHype
SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning—0
SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
Solvable Model for Inheriting the Regularization through Knowledge Distillation—0
Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks—0
Solving Large-scale Spatial Problems with Convolutional Neural Networks—0
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks—0
Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher—0
Source Data Selection for Brain-Computer Interfaces based on Simple Features—0
Source data selection for out-of-domain generalization—0
Source-Free Cross-Modal Knowledge Transfer by Unleashing the Potential of Task-Irrelevant Data—0
Source-Free Domain Adaptation for Semantic Segmentation—0
Source-free Domain Adaptation Requires Penalized Diversity—0
Source-Free Unsupervised Domain Adaptation: A Survey—0
Brain informed transfer learning for categorizing construction hazards—0
Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression—0
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing—0
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Color Editing—0
SpaceMeshLab: Spatial Context Memoization and Meshgrid Atrous Convolution Consensus for Semantic Segmentation—0
Brain-mediated Transfer Learning of Convolutional Neural Networks—0
Space-Time Graph Neural Networks with Stochastic Graph Perturbations—0
SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing—0
Brain MRI detection by Sematic Segmentation models- Transfer Learning approach—0
Adapting Task-Oriented Dialogue Models for Email Conversations—0
SPARK: Self-supervised Personalized Real-time Monocular Face Capture—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