When comparing two deep learning models, how should Cross-Entropy Loss be interpreted?

Cross-Entropy Loss is a key metric used to evaluate the performance of classification models in deep learning. It provides a measure of how well the probability distributions predicted by the model align with the actual labels. When comparing two deep learning models, a lower Cross-Entropy Loss generally indicates that the model is making more accurate predictions, as it suggests that the predicted probabilities are closer to the true class labels. This direct relationship with model accuracy is why lower loss values are preferred and signify a better-performing model.

Assessing model performance via Cross-Entropy Loss allows practitioners to determine which model is more effective in categorizing data. Thus, choosing the model with the lowest Cross-Entropy Loss is indicative of a stronger model that is likely to generalize better to unseen data, assuming other factors like overfitting are also controlled.

Other options present contrasting interpretations, such as associating low loss with underfitting or suggesting higher loss indicates better complexity, which misrepresents the relationship between loss and model performance in the context of deep learning. Neglecting the significance of loss values as indicators of model utility is also misleading, as it fails to recognize the crucial role of loss metrics in guiding model selection and improvement.