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Neural networks are commonly used as models for classification for a widevariety of tasks. Typically, a learned affine transformation is placed at theend of such models, yielding a per-class value used for classification. Thisclassifier can have a vast number of parameters, which grows linearly with thenumber of possible classes, thus requiring increasingly more resources. In thiswork we argue that this classifier can be fixed, up to a global scale constant,with little or no loss of accuracy for most tasks, allowing memory andcomputational benefits. Moreover, we show that by initializing the classifierwith a Hadamard matrix we can speed up inference as well. We discuss theimplications for current understanding of neural network models.

Fix your classifier: the marginal value of training the last weight layer