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 9101–9125 of 10307 papers

TitleStatusHype
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal PredictionCode0
Federated Deep Reinforcement Learning—0
Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural NetworksCode0
Transfer-Learning Oriented Class Imbalance Learning for Cross-Project Defect Prediction—0
Towards Compact ConvNets via Structure-Sparsity Regularized Filter PruningCode0
Programmable Neural Network Trojan for Pre-Trained Feature Extractor—0
Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning—0
Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks—0
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text—0
Transfer Learning for Image-Based Malware ClassificationCode0
Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]—0
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems—0
Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industry—0
Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails—0
Truly Generalizable Radiograph Segmentation with Conditional Domain AdaptationCode0
Transfer Learning for Prosthetics Using Imitation LearningCode0
Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data—0
Disease Knowledge Transfer across Neurodegenerative DiseasesCode0
Automatic detection of passable roads after floods in remote sensed and social media data—0
Low-Cost Transfer Learning of Face TasksCode0
Estimating Buildings' Parameters over Time Including Prior KnowledgeCode0
Transfer Representation Learning with TSK Fuzzy System—0
Supervised Transfer Learning for Product Information Question Answering—0
Multi-Source Transfer Learning for Non-Stationary EnvironmentsCode0
Vector representations of text data in deep 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