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 4551–4575 of 10307 papers

TitleStatusHype
NetCut: Real-Time DNN Inference Using Layer Removal—0
Network-Agnostic Knowledge Transfer for Medical Image Segmentation—0
Network-Agnostic Knowledge Transfer from Latent Dataset for Medical Image Segmentation—0
Network Anomaly Detection Using Federated Learning and Transfer Learning—0
Network-Based Transfer Learning Helps Improve Short-Term Crime Prediction Accuracy—0
Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification—0
Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning—0
Network Wide Evacuation Traffic Prediction in a Rapidly Intensifying Hurricane from Traffic Detectors and Facebook Movement Data: A Deep Learning Approach—0
NeuPL: Neural Population Learning—0
Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models—0
Neural Architecture Search using Particle Swarm and Ant Colony Optimization—0
Neural Collapse: A Review on Modelling Principles and Generalization—0
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning—0
Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data—0
Neural Entity Linking on Technical Service Tickets—0
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?—0
Neural Machine Translation in Low-Resource Setting: a Case Study in English-Marathi Pair—0
Neural Network based Deep Transfer Learning for Cross-domain Dependency Parsing—0
Neural Networks can Learn Representations with Gradient Descent—0
Neural Networks Trained to Solve Differential Equations Learn General Representations—0
Neural Network Training Using _1-Regularization and Bi-fidelity Data—0
Neural Paraphrase Generation using Transfer Learning—0
Neural Population Learning beyond Symmetric Zero-sum Games—0
Neural Program Meta-Induction—0
neuralRank: Searching and ranking ANN-based model repositories—0
Show:102550
← PrevPage 183 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