I originally trained multiple individual binary classifiers for each label of an image. Then, I realized I can train a single multilabel model for this task. I used binary_cross_entropy loss for this instead of categorical_cross_entropy, but besides for changing the loss function I made no major changes. However, I find that the multilabel classifier still substantially underperforms the individual label classifiers. Is this common and to be expected? Are there any tricks I am missing?