For labeled binary classification tasks, we can use the cross-entropy loss function to evaluate the difference between the model's predictions and the true labels. During the training process, by adjusting the model parameters, the value of the cross-entropy loss function is minimized, thereby improving the prediction accuracy of the model.
However, only using the cross-entropy loss function may not meet our needs, because it only focuses on the prediction accuracy of the model, without considering the distance of the feature distribution. If the feature distributions of two classes are close, the model may get confused in its predictions.
To solve this problem, we can use the idea of contrastive learning to optimize the model. Contrastive learning increases the generalization ability of classifiers by learning to map samples of the same category into tight spatial regions and map samples of different categories into mutually separated spatial regions. Therefore, we can apply the idea of contrastive learning to labeled binary classification tasks to make the feature distributions between different categories more separated.
Specifically, we can use Siamese networks or Triplet networks to achieve contrastive learning. These networks can map samples of the same class into the same feature vector space, and map samples of different classes into different feature vector spaces. We can optimize the network by minimizing the distance between samples of the same class and maximizing the distance between samples of different classes. This process can be achieved by modifying the loss function.
Common contrastive learning loss functions include:
- Triplet Loss: Triplet Loss is a contrastive learning loss function using triplet samples. For each sample, it selects two similar samples and one dissimilar sample, thus forming a triplet. The goal of Triplet Loss is to make the distance between samples of the same category as close as possible, while the distance between samples of different categories is as far as possible. The formula for Triplet Loss is as follows:
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