Visualizing Classification Structure of Large-Scale Classifiers

Both pieces of work mentioned in Section 1 rely on confusion We propose a measure to compute class similarity matrices to analyze classification structure (Alsallakh in large-scale classification based on prediction et al., 2018a; Deng et al., 2010). When ordered according to scores. Such measure has not been formally proposed the ImageNet synset hierarchy, this matrix captures the majority in the literature. We show how visualizing of confusions in few diagonal blocks that correspond the class similarity matrix can reveal hierarchical to coarse similarity groups. Each of these blocks, in turn, structures and relationships that govern the can exhibit a nested block pattern that corresponds to narrower classes.