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 5151–5175 of 10307 papers

TitleStatusHype
RCMNet: A deep learning model assists CAR-T therapy for leukemia—0
Reacting like Humans: Incorporating Intrinsic Human Behaviors into NAO through Sound-Based Reactions to Fearful and Shocking Events for Enhanced Sociability—0
READ: Recurrent Adaptation of Large Transformers—0
Realized Volatility Forecasting for New Issues and Spin-Offs using Multi-Source Transfer Learning—0
Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation—0
Real-Time And Robust 3D Object Detection with Roadside LiDARs—0
Real-time Detection of 2D Tool Landmarks with Synthetic Training Data—0
Real-time detection of uncalibrated sensors using Neural Networks—0
Real-Time Load Estimation for Load-lifting Exoskeletons Using Insole Pressure Sensors and Machine Learning—0
Real-Time Mask Detection Based on SSD-MobileNetV2—0
Real-time Plant Health Assessment Via Implementing Cloud-based Scalable Transfer Learning On AWS DeepLens—0
Real-time Sign Language Recognition Using MobileNetV2 and Transfer Learning—0
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network—0
Real-world Mapping of Gaze Fixations Using Instance Segmentation for Road Construction Safety Applications—0
Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings—0
Recent Advancements and Challenges of Turkic Central Asian Language Processing—0
Recent Advances in Optimal Transport for Machine Learning—0
Recent Advances of Foundation Language Models-based Continual Learning: A Survey—0
Recent Neural Methods on Dialogue State Tracking for Task-Oriented Dialogue Systems: A Survey—0
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey—0
rECGnition_v1.0: Arrhythmia detection using cardiologist-inspired multi-modal architecture incorporating demographic attributes in ECG—0
RECLIP: Resource-efficient CLIP by Training with Small Images—0
Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks—0
Recognition of Cardiac MRI Orientation via Deep Neural Networks and a Method to Improve Prediction Accuracy—0
Recognition of Harmful Phytoplankton from Microscopic Images using 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