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Synthetic images aid the recognition of human-made art forgeries
In addition, we find that, in line with previous research, the inclusion of synthetic forgeries in the training also enables the detection of AI-generated forgeries, especially if created using a similar generator.
Art Authentication with Vision Transformers
In recent years, Transformers, initially developed for language, have been successfully applied to visual tasks.
A one-armed CNN for exoplanet detection from light curves
We propose Genesis, a one-armed simplified Convolutional Neural Network (CNN)for exoplanet detection, and compare it to the more complex, two-armed CNN called Astronet.
Reducing Artificial Neural Network Complexity: A Case Study on Exoplanet Detection
Our results show only a non-substantial loss in accuracy compared to the original AstroNet, while reducing training time up to 85 percent.
Light-weight pixel context encoders for image inpainting
In this work we propose Pixel Content Encoders (PCE), a light-weight image inpainting model, capable of generating novel con-tent for large missing regions in images.
Learning scale-variant and scale-invariant features for deep image classification
This leads to the conclusion that encouraging the combined development of a scale-invariant and scale-variant representation in CNNs is beneficial to image recognition performance.
Exploring the influence of scale on artist attribution
Previous work has shown that the artist of an artwork can be identified by use of computational methods that analyse digital images.
Circle-based Eye Center Localization (CECL)
The CECL method achieved an accuracy of 80. 8% to 99. 4% and ranked first for 2 of the 5 thresholds.
