Zero-Shot Learning
**Example:** Suppose we have a dataset of images of cats, dogs, and birds. We want to train a model to classify new images as either cats, dogs, or birds. However, we only have access to the images in the dataset and no labels. How can we train the model to make accurate predictions on new images?
**Constraints:** The model should be able to learn from the dataset and make accurate predictions on new images. The model should not require any labeled data.
Test Cases
Test Case 1
Input:
[[1, 2, 0], [3, 4, 1], [5, 6, 2]]Expected:
0Test Case 2
Input:
[[7, 8, 0], [9, 10, 1], [11, 12, 2]]Expected:
1+ 3 hidden test cases