The bias problem in classification tasks and the different strategies used for bias mitigation. How these strategies are grouped into categories and a brief introduction of the most representative ...

Abstract: Satellite video multi-label scene classification predicts semantic labels of multiple ground contents to describe a given satellite observation video, which plays an important role in ...

The precise identification of retinal disorders is of utmost importance in the prevention of both temporary and permanent visual impairment. Prior research has yielded encouraging results in the ...

We present a large-scale multi-species dataset of acoustics recordings of amphibians anuran from PAM recordings. The dataset comprises 27 hours of herpetologist annotations of 42 different species in ...

Abstract: A multi-label image classification is a challenging task as it has to map an input image to a vector of outputs. This work presents a single and efficient model to perform multi-label and ...

Decision trees are useful for relatively small datasets that have a relatively simple underlying structure, and when the trained model must be easily interpretable, explains Dr. James McCaffrey of ...

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater ...

Multi-label classification with SimCLR is available. See another repo of mine PyTorch Image Models With SimCLR. You would get higher accuracy when you train the model with classification loss together ...