You are training a supervised image classifier to detect cats. You have 1,000 total images and a fixed test set of 100 images. You observe that over 75% of misclassifications occur when owners hold cats upside down. Which technique will specifically reduce this type of error?

The problem states that a significant portion of misclassifications occurs when cats are held upside down. This indicates that the model has not learned to generalize well to rotated images of cats. Increasing the training data by adding images with rotation variations (data augmentation) directly addresses this by exposing the model to more examples of cats in different orientations, thereby improving its ability to correctly classify them. Increasing the number of training epochs might help the model converge further, but it won't specifically address the lack of rotational invariance if the training data itself lacks such variations. Adding more layers to the neural network increases model complexity, which could lead to overfitting or might not solve the specific rotational invariance issue without appropriate data. Increasing the dropout rate helps prevent overfitting but does not directly teach the model to recognize rotated objects.