  Multitask learning is a popular approach to training high-performing neural
networks with improved generalization. In this paper, we propose a background
class to achieve improved generalization at a lower computation compared to
multitask learning to help researchers and organizations with limited
computation power. We also present a methodology for selecting background
images and discuss potential future improvements. We apply our approach to
several datasets and achieve improved generalization with much lower
computation. Through the class activation mappings (CAMs) of the trained
models, we observed the tendency towards looking at a bigger picture with the
proposed model training methodology. Applying the vision transformer with the
proposed background class, we receive state-of-the-art (SOTA) performance on
STL-10, Caltech-101, and CINIC-10 datasets. Example scripts are available in
the 'CAM' folder of the following GitHub Repository: github.com/dipuk0506/UQ
