Description: A soft target refers to a probability distribution over classes rather than a hard label. In the context of machine learning, this means that instead of assigning a specific class to an input, a probability is assigned to each possible class. This technique allows the model to capture the inherent uncertainty in predictions, which can be particularly useful in situations where decisions are not absolute. For example, in an image classification problem, a soft target might indicate that an image has a 70% probability of being a cat and a 30% probability of being a dog, rather than strictly classifying it as either a cat or a dog. This probabilistic representation not only enhances the robustness of the model but also facilitates learning in scenarios where classes may overlap or where there is noise in the data. Furthermore, soft targets are fundamental in techniques such as transfer learning and semi-supervised learning, where the goal is to leverage additional information to improve model accuracy. In summary, soft targets allow for greater flexibility and adaptability in machine learning models, resulting in more effective performance on complex tasks.
