What does "Weighted Losses" mean?

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Weighted losses are methods used in training models, especially when dealing with data that is not balanced. In many situations, some categories of data are less common than others. For example, if a model is trained to recognize animals, there may be many images of cats but only a few of rare animals like pandas. This imbalance can make it hard for the model to learn properly.

To help the model pay more attention to the less common categories, weighted losses assign more importance to those categories during training. This way, the model learns to not ignore the rare cases. By doing this, it can make better predictions across all categories, not just the most common ones.

These methods are particularly useful in situations where the goal is to ensure that every category is fairly represented in the model's learning process. They help make the model more fair and accurate by balancing out the influence of different types of data.