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Found 4 papers, 2 papers with code
Analyzing Effects of Fake Training Data on the Performance of Deep Learning Systems
This is especially the case for computer vision models.
Performance evaluation of deep segmentation models for Contrails detection
In this work, we benchmark several popular segmentation models with combinations of different loss functions and encoder backbones.
UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection
Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy.
Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Data
The magnitude of a model's performance is proportional to this shift in the distribution of the dataset.
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