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 8651–8675 of 10307 papers

TitleStatusHype
P2L: Predicting Transfer Learning for Images and Semantic Relations—0
An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement LearningCode0
Latent User Linking for Collaborative Cross Domain Recommendation—0
Cross-Enhancement Transform Two-Stream 3D ConvNets for Action Recognition—0
Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews—0
Transfer in Deep Reinforcement Learning using Knowledge Graphs—0
Transfer Learning-Based Label Proportions Method with Data of Uncertainty—0
Language Graph Distillation for Low-Resource Machine Translation—0
Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach—0
Regularizing CNN Transfer Learning with Randomised Regression—0
Transferable Contrastive Network for Generalized Zero-Shot Learning—0
Multitask and Transfer Learning for Autotuning Exascale Applications—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
Skill Transfer in Deep Reinforcement Learning under Morphological Heterogeneity—0
Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning—0
End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning—0
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs—0
Experience Reuse with Probabilistic Movement Primitives—0
UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task DistillationCode0
A Generate-Validate Approach to Answering Questions about Qualitative Relationships—0
Neural Network based Deep Transfer Learning for Cross-domain Dependency Parsing—0
Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval—0
Progressive Transfer LearningCode0
Fine-Tuning Models Comparisons on Garbage Classification for Recyclability—0
Relative Afferent Pupillary Defect Screening through 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