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Found 8 papers, 3 papers with code
SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks
For many years, it has been shown how much exploiting equivariances can be beneficial when solving image analysis tasks.
Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial Networks
The model would be able to identify its weaknesses by better learning how to transform an abnormal (respectively normal) image into a normal (respectively abnormal) one, helping the entire model to learn better than a single normal to normal reconstruction.
Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset
Nevertheless, several challenges have to be faced, including imbalanced datasets, the image complexity, and the zero-false-negative (ZFN) constraint to guarantee the high-quality requirement.
Achieving Rotational Invariance with Bessel-Convolutional Neural Networks
For many applications in image analysis, learning models that are invariant to translations and rotations is paramount.
LSFB-CONT and LSFB-ISOL: Two New Datasets for Vision-Based Sign Language Recognition
While significant progress have been made in the field of Natural Language Processing (NLP), leading the commercially available products, Sign Language Recognition (SLR) is still in its infancy.
DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality
A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all the more so given some techniques come with additional parametrization (e. g. t-SNE, UMAP, etc.).
Impact of Legal Requirements on Explainability in Machine Learning
The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear.
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
These new examples are then used to retrain and improve the model in the first step.
