If we have sufficient training data of every class, e.g., “dog” and “cat” images with different shapes, poses, colors, and backgrounds (i.e., in different environments), by using a conventional softmax cross-entropy based “dog vs. cat” classifier, we can obtain a perfect “dog-cat” model. However, we don’t have such training data in reality, and need to learn models from insufficient data. In this keynote, we will talk about why insufficient data renders the model easily biased to the limited environments in training data; and how to do invariant learning that learns the inherent causality of image recognition and yields generalizable models to the different environments in testing data.