Soft label cross entropy

1. What is label smoothing cross-entropy?
2. What is soft labeling?
3. What is Softmax cross-entropy?
4. Can cross-entropy loss be robust to label noise?

What is label smoothing cross-entropy?

It is binary in nature (0 or 1) For binary cross-entropy loss, we convert the hard labels into soft labels by applying a weighted average between the uniform distribution and the hard labels. Label smoothing is often used to increase robustness and improve classification problems.

What is soft labeling?

Soft labels indicate the degree of membership of the training data to the given classes. Often only a small number of labeled data is available while unlabeled data is abundant. Therefore, it is important to make use of unlabeled data.

What is Softmax cross-entropy?

Softmax Activation Function — How It Actually Works

In the above Figure, Softmax converts logits into probabilities. The purpose of the Cross-Entropy is to take the output probabilities (P) and measure the distance from the truth values (as shown in Figure below).

Can cross-entropy loss be robust to label noise?

In this paper, we propose a general frame- work dubbed Taylor cross entropy loss to train deep models in the presence of label noise. Specifically, our framework enables to weight the extent of fit- ting the training labels by controlling the order of Taylor Series for CCE, hence it can be robust to label noise.