A 1x1 convolutional filter is a type of filter that is commonly used in convolutional neural networks (CNNs) for image processing and computer vision tasks. Unlike traditional filters which have dimensions like 3x3 or 5x5, a 1x1 convolutional filter has only one row and one column, hence the name "1x1".
Although it may seem counterintuitive that such a small filter can be effective, 1x1 convolutions have a number of advantages. For one, they can be used to reduce the number of feature maps produced by a CNN, which can help to reduce computational complexity and memory requirements. This process is known as "channel-wise pooling" or "bottlenecking", and involves applying a 1x1 convolution to the input feature maps, followed by a non-linear activation function.
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