In this paper, we propose a method to improve image classification performance using the fusion of CNN and transformer structure. case CNN, information about local area on an can be extracted well, but global extraction is limited. On other hand, has advantage in extraction, it requires much memory compared CNN. We apply consider feature vector each pixel resulting map by as token. At same time...

Abstract The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way. A counterfactual explanation highlights parts which, when removed, would change predicted class. Both legal scholars and data scientists are increasingly turning explanations as these provide a high degree human interpretability, reve...

Purpose Because of the fast-growing digital image collections on online platforms and transfer learning ability deep technology, classification could be improved implemented for hostel domain, which has complex clusters contents. This paper aims to test potential 11 pretrained convolutional neural network (CNN) with first database advance knowledge fill gap academically, as well suggest an alte...

Convolutional Neural Networks (CNN) have become de facto state-of-the-art for the main computer vision tasks. However, due to complex underlying structure their decisions are hard understand which limits use in some context of industrial world. A common and detect challenge machine learning (ML) tasks is data bias. In this work, we present a systematic approach uncover bias by means attribution...