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Workshop: I Can’t Believe It’s Not Better: Understanding Deep Learning Through Empirical Falsification

Paradigmatic Revolutions in Computer Vision

Andreas Kriegler


Kuhn's groundbreaking Structure divides scientific progress into four phases, the pre-paradigm period, normal science, scientific crisis and revolution. Most of the time a field advances incrementally, constrained and guided by a currently agreed upon paradigm following an implicit set of rules. Creative phases emerge when phenomena occur which lack satisfactory explanation within the current paradigm (the crisis) until a new one replaces it (the revolution). This model of science was mainly laid out by exemplars from natural science, while we want to show that Kuhn's work is also applicable for information sciences. We analyze the state of one field in particular, computer vision, using Kuhn's vocabulary. Following significant technology-driven advances of machine learning methods in the age of deep learning, researchers in computer vision were eager to accept the models that now dominate the state of the art. We discuss the current state of the field especially in light of the deep learning revolution and argue that current deep learning methods cannot fully constitute a paradigm for computer vision in the Kuhnian sense.

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