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Found 6 papers, 1 papers with code
Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models
Fine-tuning the pre-trained model with active learning holds promise for reducing annotation costs.
Metrically Scaled Monocular Depth Estimation through Sparse Priors for Underwater Robots
In this work, we address the problem of real-time dense depth estimation from monocular images for mobile underwater vehicles.
NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage
Recent research has shown that in the context of supervised learning different active learning strategies need to be applied at various stages of the training process to ensure improved performance over the random baseline.
Contactless hand tremor amplitude measurement using smartphones: development and pilot evaluation
Objective: to develop a proof of principle method to measure hand tremor amplitude from smartphone videos.
Training from a Better Start Point: Active Self-Semi-Supervised Learning for Few Labeled Samples
Consequently, our framework can significantly improve the performance of models in the case of few annotations while reducing the training time.
Analysis of an adaptive lead weighted ResNet for multiclass classification of 12-lead ECGs
Method: We proposed a squeeze and excite ResNet to automatically learn deep features from 12-lead ECGs, in order to identify 24 cardiac conditions.
