When an object is partially obscured by another object, it is referred to as "occluded." Simulating occlusion is crucial because it prevents your model from overly relying on a single distinctive feature for identification. For example, by occluding a cat’s ears, the model is compelled to learn about other features like its paws and tail instead of relying solely on the ears. This is particularly helpful in real-world scenarios where, for instance, a cat’s head might be hidden by a chair.
