LEURN: a neural network architecture that learns univariate decision rules

Face recognition systems have demonstrated remarkable performances and thus gained a vital role in our daily life. They already surpass human face verification accountability in...

We build two explainable style transfer datasets by augmenting existing datasets with synthetic textual explanations generated by a teacher model.

We build two explainable style transfer datasets by augmenting existing datasets with synthetic textual explanations generated by a teacher model.

We build two explainable style transfer datasets by augmenting existing datasets with synthetic textual explanations generated by a teacher model.

DermXDB is a skin disease diagnosis explainability dataset which enables benchmarking the explainability performance of ConvNet architectures.

The dataset is used for building a music recommendation product with key focus on new song discovery and explainability.

The HateXplain dataset is a benchmark dataset for explainable hate speech detection.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the ImageNet, ImageNetV2, ImageNet-Sketch, ImageNet-A, and Imagenet-R datasets.

The dataset used in the paper is a collection of images from various sources, including MNIST, EMNIST, CIFAR10, X-ray pneumonia, and Diabetic Retinopathy detection.