👉 Contrast engineering is a technique used in machine learning, particularly in deep learning and computer vision, to enhance the performance of models by strategically manipulating the input data to highlight differences between classes. This is achieved by creating contrastive pairs—images that are similar within the same class but distinct across different classes—and training the model to maximize the similarity between these pairs while minimizing the similarity between them. By doing so, contrast engineering helps the model learn more robust and discriminative features, improving its ability to generalize and make accurate predictions. This approach is especially useful in scenarios where labeled data is scarce or imbalanced, as it leverages the inherent structure of the data to improve model performance.
