Explain convolution in CNNs.
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In Convolutional Neural Networks (CNNs), convolution is the core operation that helps extract features from images. Instead of looking at the entire image at once, convolution applies a small filter (kernel) that slides over the image and performs element-wise multiplication with pixel values. The sum of these multiplications produces a new value, forming a feature map. This process highlights important patterns like edges, textures, or shapes.
For example, a 3×3 filter might detect vertical edges by assigning higher weights to certain pixel alignments. As the filter moves (strides) across the image, it captures local features while reducing complexity. Multiple filters can be applied in parallel to detect different patterns.
Convolution helps CNNs learn hierarchical features: the first layers detect simple edges, middle layers detect shapes, and deeper layers capture complex objects. This makes CNNs robust for tasks like image classification, object detection, and face recognition.
Unlike fully connected layers, convolution reduces parameters, preventing overfitting and making the model efficient. In short, convolution acts as the feature extractor that converts raw pixels into meaningful representations for learning.
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