WEEK 3: What's difference between hard predictions and hard labels?

In Distillation technique, what’s difference between hard predictions and hard labels? I am confused about what the student model actually done. Thanks!

  1. Hard Predictions:
  • Hard predictions refer to the outputs or predictions made by the teacher model (often a larger, pre-trained model) when it’s provided with a set of input data. These predictions are typically probability distributions over different classes or categories, generated by applying a softmax function to the model’s logits. Hard predictions are essentially what the teacher model believes to be the correct class labels for the given inputs.
  1. Hard Labels:
  • Hard labels, on the other hand, are the actual ground truth labels for the training data. These labels are fixed and represent the correct classes or categories for each input sample. In supervised learning, the model’s goal during training is to make its predictions (soft or hard) as close as possible to these hard labels.

Now, in the context of the Distillation technique, the main idea is to train a smaller student model to mimic the behavior of the larger teacher model. This is typically done by using both hard predictions and soft predictions (also known as logits) from the teacher model. Here’s how it works:

  • Using Hard Predictions: The student model is trained to match the hard predictions of the teacher model. In this case, the student’s objective is to produce the same class labels as the teacher model for each input sample. This helps the student model learn from the teacher’s knowledge about the dataset.
  • Using Soft Predictions (Logits): In addition to matching hard predictions, the student model is often trained to also match the soft predictions (logits) produced by the teacher model. These logits provide information about the uncertainty or confidence of the teacher model’s predictions. By matching the logits, the student can learn not only the correct class labels but also the teacher’s confidence in those labels.

The use of both hard and soft targets (predictions) allows the student model to capture both the fine-grained details of the teacher’s knowledge (soft predictions) and the actual class labels (hard predictions). This can lead to improved generalization and performance, especially when the teacher model is a well-trained, large model with valuable knowledge about the dataset.

In summary, the difference between hard predictions and hard labels lies in their origin and purpose: hard labels are the ground truth labels of the training data, while hard predictions are what the teacher model believes are the correct labels for the same data. The student model in distillation aims to learn from both of these sources of information to improve its performance and generalize better.