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 2301–2325 of 10307 papers

TitleStatusHype
A Domain Adaptation Regularization for Denoising Autoencoders—0
An information-Theoretic Approach to Semi-supervised Transfer Learning—0
Brain Tumor Detection Using Deep Learning Approaches—0
Tuned Inception V3 for Recognizing States of Cooking Ingredients—0
Deep Job Understanding at LinkedIn—0
Brain Tumor Classification on MRI in Light of Molecular Markers—0
BrainTalker: Low-Resource Brain-to-Speech Synthesis with Transfer Learning using Wav2Vec 2.0—0
An Information-Theoretic Approach to Transferability in Task Transfer Learning—0
Brain MRI detection by Sematic Segmentation models- Transfer Learning approach—0
On the Generalization for Transfer Learning: An Information-Theoretic Analysis—0
Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images—0
Brain-mediated Transfer Learning of Convolutional Neural Networks—0
Brain informed transfer learning for categorizing construction hazards—0
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher—0
Abnormal Event Detection in Urban Surveillance Videos Using GAN and Transfer Learning—0
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition—0
Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals—0
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ—0
Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors—0
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks—0
Towards Complementary Knowledge Distillation for Efficient Dense Image Prediction—0
An Improvement for Capsule Networks using Depthwise Separable Convolution—0
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review—0
An Improved Model for Diabetic Retinopathy Detection by using Transfer Learning and Ensemble Learning—0
Show:102550
← PrevPage 93 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