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 5651–5675 of 10307 papers

TitleStatusHype
Social Learning: Towards Collaborative Learning with Large Language Models—0
SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization—0
Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis—0
Soft Representation Learning for Sparse Transfer—0
Software Vulnerability Prediction Knowledge Transferring Between Programming Languages—0
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
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
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
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
Space-Time Graph Neural Networks with Stochastic Graph Perturbations—0
SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing—0
SPARK: Self-supervised Personalized Real-time Monocular Face Capture—0
Sparse annotation strategies for segmentation of short axis cardiac MRI—0
Sparse Array Design for Direction Finding using Deep Learning—0
Show:102550
← PrevPage 227 of 413Next →
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