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Found 6 papers, 4 papers with code
Learning to Detect Interesting Anomalies
This approach, AHUNT, shows excellent performance on MNIST, CIFAR10, and Galaxy-DESI data, significantly outperforming both standard anomaly detection and active learning algorithms with static feature spaces.
U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation
Finally, our experiments show that models that have exploited attention modules successfully overcome common skin lesion segmentation problems.
Recommendations on test datasets for evaluating AI solutions in pathology
The recommendations are intended to help AI developers demonstrate the utility of their products and to help regulatory agencies and end users verify reported performance measures.
Deep Learning improves identification of Radio Frequency Interference
Our results strongly suggest that deep learning on simulations, boosted by transfer learning on real data, will likely play a key role in the future of RFI flagging of radio astronomy data.
Instrumentation and Methods for Astrophysics
Inpainting via Generative Adversarial Networks for CMB data analysis
In this work, we propose a new method to inpaint the CMB signal in regions masked out following a point source extraction process.
A Flexible Framework for Anomaly Detection via Dimensionality Reduction
Anomaly detection is challenging, especially for large datasets in high dimensions.
