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Convolutional layers learn features by sliding shared filters over local receptive fields to produce activation (feature) maps.
Convolutional layers are the core building blocks of convolutional neural networks (CNNs). They take an input tensor (height, width, and channels) and apply learnable filters (kernels) across local receptive fields, producing an activation/feature map for the next layer. Each neuron in a convolutional layer only “sees” a restricted region of the previous layer (its receptive field), which makes CNNs practical for large inputs like images by greatly reducing the number of parameters compared with fully connected layers. A key idea in convolutional layers is parameter sharing: the same filter weights and bias are reused across different spatial locations. This reduces memory and computation, helps training stability (mitigating vanishing/exploding gradients), and yields translation-equivariant responses (feature maps shift in a predictable way when the input shifts). Convolutional layers also depend on hyperparameters such as kernel size, stride, and padding, which determine the spatial size of the output feature maps. Convolutional layers can be extended or modified for efficiency and larger context. For example, depthwise separable convolutions replace standard convolution with a depthwise spatial convolution per channel followed by a pointwise (1×1) convolution. Dilated (atrous) convolutions expand the receptive field without increasing parameters by inserting “holes” between sampled positions, enabling multi-layer context growth more efficiently.
Convolutional layers learn features by sliding shared filters over local receptive fields to produce activation (feature) maps.
Parameter sharing and local connectivity reduce the number of free parameters and improve training efficiency compared with fully connected layers.
Output size is controlled by kernel size, stride, and padding; dilated and depthwise separable variants adjust receptive field and computational cost.
A CNN layer that applies learnable filters across the input’s spatial dimensions to produce feature maps.
A small learnable weight matrix (or tensor) used to compute responses over local regions of the input.
The output of a convolutional layer showing the filter responses across spatial locations.
The limited region of the previous layer that a single neuron in a convolutional layer is connected to.
The practice of using the same filter weights and bias across multiple spatial positions in a convolutional layer.
The step size with which the convolutional filter moves across the input, affecting output resolution.
Extra zeros (or other padding) added to the input border so the filter can be applied near edges and to control output size.
A convolution that expands the receptive field by sampling input positions more sparsely using dilation.
An efficient convolution factorization that performs depthwise convolution per channel followed by a pointwise (1×1) convolution.
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