You work for a manufacturing company. You need to train a custom image classification model to detect product defects at the end of an assembly line. Although your model is performing well, some…

Integrated Gradients is a robust feature-based explanation method for image classification models that provides pixel-level attributions, revealing which parts of an image contribute most to a specific prediction. Setting the visualization type to `GRADIENT_OVERLAY` allows for a clear visual inspection of these salient areas, helping to understand high-confidence misclassifications.

Vertex AI Matching Engine is used for similarity search in embedding spaces and does not provide explanations for *why* an image classification model made a specific prediction.

While XRAI is also an image explanation method, Integrated Gradients is a widely used and effective technique for pixel-level attribution to understand model focus, making this a plausible but not uniquely correct alternative without further context.

Example-based explanations find similar training examples but do not explain *why* the model made a specific prediction on the given input image by highlighting relevant features within it.