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Found 5 papers, 5 papers with code
NECO: NEural Collapse Based Out-of-distribution detection
Detecting out-of-distribution (OOD) data is a critical challenge in machine learning due to model overconfidence, often without awareness of their epistemological limits.
InfraParis: A multi-modal and multi-task autonomous driving dataset
Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes.
A study of deep perceptual metrics for image quality assessment
Several metrics exist to quantify the similarity between images, but they are inefficient when it comes to measure the similarity of highly distorted images.
Robust Semantic Segmentation with Superpixel-Mix
Along with predictive performance and runtime speed, reliability is a key requirement for real-world semantic segmentation.
Learning Deep Morphological Networks with Neural Architecture Search
Using a combination of linear and non-linear procedures is critical for generating a sufficiently deep feature space.
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