Should We Always Train Models on Fine-Grained Classes?
This work addresses a practical problem for machine learning practitioners by showing that fine-grained training is not always beneficial, which is incremental as it builds on prior empirical observations.
The paper investigates whether training models on fine-grained classes universally improves classification accuracy, finding that it does not; effectiveness depends on data geometry, label hierarchy relations, dataset size, and model capacity.
In classification problems, models must predict a class label based on the input data features. However, class labels are organized hierarchically in many datasets. While a classification task is often defined at a specific level of this hierarchy, training can utilize a finer granularity of labels. Empirical evidence suggests that such fine-grained training can enhance performance. In this work, we investigate the generality of this observation and explore its underlying causes using both real and synthetic datasets. We show that training on fine-grained labels does not universally improve classification accuracy. Instead, the effectiveness of this strategy depends critically on the geometric structure of the data and its relations with the label hierarchy. Additionally, factors such as dataset size and model capacity significantly influence whether fine-grained labels provide a performance benefit.