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Neural image classification models typically consist of two components. Thefirst is an image encoder, which is responsible for encoding a given raw imageinto a representative vector. The second is the classification component, whichis often implemented by projecting the representative vector onto target classvectors. The target class vectors, along with the rest of the model parameters,are estimated so as to minimize the loss function. In this paper, we analyzehow simple design choices for the classification layer affect the learningdynamics. We show that the standard cross-entropy training implicitly capturesvisual similarities between different classes, which might deteriorate accuracyor even prevents some models from converging. We propose to draw the classvectors randomly and set them as fixed during training, thus invalidating thevisual similarities encoded in these vectors. We analyze the effects of keepingthe class vectors fixed and show that it can increase the inter-classseparability, intra-class compactness, and the overall model accuracy, whilemaintaining the robustness to image corruptions and the generalization of thelearned concepts.

Redesigning the classification layer by randomizing the class representation vectors