  In recent years, supervised learning using Convolutional Neural Networks
(CNNs) has achieved great success in image classification tasks, and large
scale labeled datasets have contributed significantly to this achievement.
However, the definition of a label is often application dependent. For example,
an image of a cat can be labeled as "cat" or perhaps more specifically "Persian
cat." We refer to this as label granularity. In this paper, we conduct
extensive experiments using various datasets to demonstrate and analyze how and
why training based on fine-grain labeling, such as "Persian cat" can improve
CNN accuracy on classifying coarse-grain classes, in this case "cat." The
experimental results show that training CNNs with fine-grain labels improves
both network's optimization and generalization capabilities, as intuitively it
encourages the network to learn more features, and hence increases
classification accuracy on coarse-grain classes under all datasets considered.
Moreover, fine-grain labels enhance data efficiency in CNN training. For
example, a CNN trained with fine-grain labels and only 40% of the total
training data can achieve higher accuracy than a CNN trained with the full
training dataset and coarse-grain labels. These results point to two possible
applications of this work: (i) with sufficient human resources, one can improve
CNN performance by re-labeling the dataset with fine-grain labels, and (ii)
with limited human resources, to improve CNN performance, rather than
collecting more training data, one may instead use fine-grain labels for the
dataset. We further propose a metric called Average Confusion Ratio to
characterize the effectiveness of fine-grain labeling, and show its use through
extensive experimentation. Code is available at
https://github.com/cmu-enyac/Label-Granularity.
