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 8076–8100 of 10307 papers

TitleStatusHype
Neonatal Face and Facial Landmark Detection from Video Recordings—0
Neonatal Pain Expression Recognition Using Transfer Learning—0
A Hybrid Defense Method against Adversarial Attacks on Traffic Sign Classifiers in Autonomous Vehicles—0
Healthcare NER Models Using Language Model Pretraining—0
The Devil is in the Tails: Fine-grained Classification in the Wild—0
Nested ResNet: A Vision-Based Method for Detecting the Sensing Area of a Drop-in Gamma Probe—0
Dynamics and Reachability of Learning Tasks—0
NetCut: Real-Time DNN Inference Using Layer Removal—0
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps—0
Network-Agnostic Knowledge Transfer for Medical Image Segmentation—0
Network-Agnostic Knowledge Transfer from Latent Dataset for Medical Image Segmentation—0
The effectiveness of unsupervised subword modeling with autoregressive and cross-lingual phone-aware networks—0
Network Anomaly Detection Using Federated Learning and Transfer Learning—0
Network-Based Transfer Learning Helps Improve Short-Term Crime Prediction Accuracy—0
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning—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
SSDL: Self-Supervised Domain Learning for Improved Face Recognition—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
SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS—0
The effect of variable labels on deep learning models trained to predict breast density—0
Neural Architecture Search using Particle Swarm and Ant Colony Optimization—0
A Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning—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