Inception modules are specialized building blocks used in convolutional neural networks (CNNs) that allow for more efficient and effective feature extraction. They enable the network to capture features at multiple scales by using parallel convolutional filters of different sizes within the same layer, enhancing the model's ability to learn complex patterns without significantly increasing computational cost.
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