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You are optimizing a multimodal model for deployment on an edge device with limited memory and computational resources. The model takes video frames and audio as input to predict human actions. Which of the following optimization techniques would provide the MOST significant reduction in model size and computational complexity without drastically sacrificing accuracy?
Correct Answer: C
Knowledge distillation involves training a smaller 'student' model to mimic the behavior of a larger, more complex 'teacher' model. This can significantly reduce the size and computational complexity of the model while preserving a reasonable level of accuracy. Increasing the number of layers or using larger kernel sizes would increase complexity. Increasing audio sampling rate would increase the input data size. Removing batch normalization might reduce computational complexity slightly, but could negatively impact accuracy.