Your question is Handling Noise in Image Data. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You are training an image model and notice that the data contains noise, such as blur, compression artifacts, sensor noise, and occasional mislabeled examples. You want the model to generalize well instead of learning spurious patterns from corrupted inputs.
How would you handle noise in image data?