I think that generally it is hard to handle a “default/none/other/garbage” dedicated class.
I suppose that you could start to think about OOD that itself it is not an easy job also in Deep Learning.
But you could start to take an overview in:
  
      

      arXiv.org
  

  
    

Exploring the Limits of Out-of-Distribution Detection

  Near out-of-distribution detection (OOD) is a major challenge for deep neural
networks. We demonstrate that large-scale pre-trained transformers can
significantly improve the state-of-the-art (SOTA) on a range of near OOD tasks
across different data...