Why Causality in Machine Learning is an Problem?, Malick Sarr

However, in practice, distributions frequently shift due to factors that cannot be explored or controlled in the training data. Convolutional neural networks trained on millions of photos, for example, might fail when seeing things in new lighting conditions, from slightly altered angles, or against new backdrops. Attempts to resolve these issues include training machine learning models on more samples. However, as the environment becomes more complicated, adding additional training instances becomes impractical to cover the entire distribution.