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 7726–7750 of 10307 papers

TitleStatusHype
A microservice-based framework for exploring data selection in cross-building knowledge transfer—0
SpaceMeshLab: Spatial Context Memoization and Meshgrid Atrous Convolution Consensus for Semantic Segmentation—0
The Complex Negotiation Dialogue Game—0
MorisienMT: A Dataset for Mauritian Creole Machine Translation—0
Morpheo: Traceable Machine Learning on Hidden data—0
Space-Time Graph Neural Networks with Stochastic Graph Perturbations—0
AM Flow: Adapters for Temporal Processing in Action Recognition—0
SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing—0
Motion-Augmented Self-Training for Video Recognition at Smaller Scale—0
Motion Blur removal via Coupled Autoencoder—0
Motion-Guided Masking for Spatiotemporal Representation Learning—0
P2L: Predicting Transfer Learning for Images and Semantic Relations—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
AMF: Adaptable Weighting Fusion with Multiple Fine-tuning for Image Classification—0
SPARK: Self-supervised Personalized Real-time Monocular Face Capture—0
Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition—0
AMEX-AI-LABS: Investigating Transfer Learning for Title Detection in Table of Contents Generation—0
A Methodology for Controlling the Emotional Expressiveness in Synthetic Speech -- a Deep Learning approach—0
Moving from Cross-Project Defect Prediction to Heterogeneous Defect Prediction: A Partial Replication Study—0
Sparse annotation strategies for segmentation of short axis cardiac MRI—0
Sparse Array Design for Direction Finding using Deep Learning—0
MRIo3DS-Net: A Mutually Reinforcing Images to 3D Surface RNN-like framework for model-adaptation indoor 3D reconstruction—0
Intelligent Condition Based Monitoring Techniques for Bearing Fault Diagnosis—0
A Method of Augmenting Bilingual Terminology by Taking Advantage of the Conceptual Systematicity of Terminologies—0
A Method for Building a Commonsense Inference Dataset based on Basic Events—0
Show:102550
← PrevPage 310 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