Few-Shot Learning
**Example:** Suppose we have a small dataset of 5 images of cats and dogs. We want to train a model to classify new images as either cats or dogs. However, we only have access to the 5 images in the dataset. How can we train the model to make accurate predictions on new images?
**Constraints:** The model should be able to learn from the small dataset and make accurate predictions on new images. The model should not require a large dataset to train.
Test Cases
Test Case 1
Input:
[[[1, 2, 0], [3, 4, 1]], [[5, 6, 0], [7, 8, 1]]]Expected:
1Test Case 2
Input:
[[[9, 10, 0], [11, 12, 1]], [[13, 14, 0], [15, 16, 1]]]Expected:
0+ 3 hidden test cases