OneCAD: One Classifier for All image Datasets using multimodal learning. (arXiv:2305.07167v1 [cs.CV])

Vision-Transformers (ViTs) and Convolutional neural networks (CNNs) are
widely used Deep Neural Networks (DNNs) for classification task. These model
architectures are dependent on the number of classes in the dataset it was
trained on. Any change in number of classes leads to change (partial or full)
in the model's architecture. This work addresses the question: Is it possible
to create a number-of-class-agnostic model architecture?. This allows model's
architecture to be independent of the dataset it is trained on. This work …

architectures arxiv change classification classifier cnns convolutional neural networks dataset datasets image image datasets leads multimodal multimodal learning networks neural networks transformers vision