Discover the correct statement about the Tokyo computer vision model and how training with diverse datasets can improve its performance. Expert insights for the Google AI for Anyone certification exam.
Table of Contents
Question
Identify the TRUE statement regarding the Tokyo computer vision model.
A. The model successfully generated the lower half of a white face even though it was trained only on Asian faces.
B. The model can be improved by training with datasets of both Asian and white faces.
C. This is an example of overfitting.
Answer
The correct statement regarding the Tokyo computer vision model is:
B. The model can be improved by training with datasets of both Asian and white faces.
Explanation
The Tokyo computer vision model was trained on a dataset consisting only of Asian faces. As a result, when given an image of a white face with the lower half missing, it struggled to accurately generate the missing portion. This is because the model had not been exposed to a diverse range of faces during training.
To improve the model’s performance and generalization capabilities, it is essential to train it using datasets that encompass a wide variety of faces, including both Asian and white faces. By exposing the model to a more diverse and representative dataset, it can learn the underlying patterns and features common to all human faces, regardless of race or ethnicity.
Training with diverse datasets helps the model avoid overfitting, which occurs when a model becomes too specialized to the training data and fails to generalize well to new, unseen examples. By incorporating a broader range of faces during training, the model can develop a more robust understanding of facial features and structures, enabling it to generate accurate results for a wider range of input images.
In summary, the Tokyo computer vision model’s limitations can be addressed by training it with datasets that include both Asian and white faces, promoting better generalization and reducing the risk of overfitting.
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