The dimensions of a convolution kernel are defined by its height ($$h$$) and width ($$w$$), commonly referred to as an $$h 	imes w$$ convolution kernel. Accordingly, the operation utilizing this kernel is known as an $$h 	imes w$$ convolution. Furthermore, a convolutional layer that employs an $$h 	imes w$$ convolution kernel is simply denoted as an $$h 	imes w$$ convolutional layer.

Claude

University of Michigan - Ann Arbor

A “filter”, sometimes called a “kernel”, is passed over the image, viewing a few pixels at a time (for example, 3X3 or 5X5). The convolution operation is a dot product of the original pixel values with weights defined in the filter. The results are summed up into one number that represents all the pixels the filter observed.

Convolution Filter (Kernel)

A convolutional layer performs a cross-correlation operation between its input and a kernel, and subsequently adds a scalar bias to the result to generate an output. During model training, the convolutional layer learns two key parameters: the kernel (weights) and the scalar bias. Prior to training, these kernel weights are generally initialized with random values, analogous to the initialization process in fully connected layers.

Convolutional Layer

Dive into Deep Learning

Good article for buliding an intuition on how to derive the backpropagation formula:https://medium.com/@pavisj/convolutions-and-backpropagations-46026a8f5d2c

Convolutions and Backpropagations

The back-propagation in CNN is done on the same principle of the chain rule, but the the the way it is actually done is not that easy. Of course if we have the gradient of the output we could do bunch of nested loops and calculate the gradients of the parameters and inputs, but this would be crazy inefficient. We need to search a way to vectorize this. We already implemented the forward propagation, and convolution operation is heavily involved in back propagation. The formulas for the gradients of the input are the following:(Suppose L is the cost function, F is the current filter, X is the current input)


Backpropagation in CNN

The cross-correlation operation involves mapping a filter matrix (or kernel) to every possible position on an input matrix. At each position, the corresponding elements of the input and the kernel are multiplied together, and the products are summed to calculate a single entry in the resulting output matrix. While a strict mathematical convolution requires flipping the two-dimensional kernel both horizontally and vertically prior to this process, deep learning implementations typically omit this mirroring step and compute the cross-correlation directly. Despite this technical distinction, it is standard terminology in deep learning literature to refer to the cross-correlation operation simply as a convolution.

The Cross-Correlation (Convolution) Operation

https://medium.com/@RosieCampbell/demystifying-deep-neural-nets-efb726eae941

Demystifying Deep Neural Nets

Three basic strategies for obtaining convolution kernels without supervised training:
- Initializing them randomly
- Setting each kernel to detect edges at a certain orientation or scale
- Item learning the kernels with an unsupervised criterion

Obtaining Kernels without Supervised Training

Convolution Kernel and Layer Size Notation

For a convolutional layer $$l$$, the output dimensions and number of parameters are calculated as follows:

Input Size: $$n_h^{[l-1]} 	imes n_w^{[l-1]} 	imes n_c^{[l-1]}$$
Filter (Kernel) Size: $$k_h^{[l]} 	imes k_w^{[l]} 	imes n_c^{[l-1]}$$
*(Note: $$n_c^{[l]}$$ is the number of filters in layer $$l$$, matching the number of output channels.)
Stride: $$s_h^{[l]} 	imes s_w^{[l]}$$
Padding: $$p_h^{[l]} 	imes p_w^{[l]}$$

Output (Activations $$a^{[l]}$$) Size: $$n_h^{[l]} 	imes n_w^{[l]} 	imes n_c^{[l]}$$
$$n_h^{[l]} = \left \lfloor \frac{n_h^{[l-1]} + 2p_h^{[l]} - k_h^{[l]}}{s_h^{[l]}} ight floor + 1$$
$$n_w^{[l]} = \left \lfloor \frac{n_w^{[l-1]} + 2p_w^{[l]} - k_w^{[l]}}{s_w^{[l]}} ight floor + 1$$

Number of Parameters:
 Weights: $$k_h^{[l]} 	imes k_w^{[l]} 	imes n_c^{[l-1]} 	imes n_c^{[l]}$$
* Biases: $$n_c^{[l]}$$

Convolution Layer Output Size and Parameter Formulas

While it is possible to manually design simple convolution kernels for specific tasks—such as using a $$1 \times 2$$ finite difference operator like $$[1, -1]$$ for basic edge detection—this approach does not scale to complex problems. As deep learning models utilize larger kernels and incorporate successive layers of convolutions, it becomes practically impossible to manually specify the precise weights and the specific function of every individual filter.

Limitation of Manually Designed Convolutional Kernels

In deep learning, operations labeled as convolutions are technically cross-correlations. A strict two-dimensional discrete convolution uses index differences to sum elements, effectively flipping the kernel: $$(f * g)(i, j) = \sum_a \sum_b f(a, b) g(i-a, j-b)$$. In contrast, cross-correlation uses index additions: $$(i+a, j+b)$$. Because kernels are learned from data, a network using cross-correlation will simply learn a flipped version of the kernel matrix $$\mathbf{K}'$$ that a strict convolution would learn as $$\mathbf{K}$$, producing identical outputs. This cosmetic distinction justifies the standard terminology of calling cross-correlation a convolution.

Equivalence of Strict Convolution and Cross-Correlation

In the context of convolutional neural networks, a feature map is the output produced by a convolutional layer. It acts as a learned representation, capturing specific spatial features—such as patterns across height and width dimensions—that are then passed forward as input to subsequent layers in the network.

Feature Map

A two-dimensional convolutional layer can be implemented programmatically by defining a class that encapsulates its two core parameters: the kernel weights and the scalar bias. In the class constructor, these parameters are declared and initialized (e.g., initializing weights randomly and bias to zero). The layer's forward propagation method then computes the output by executing a two-dimensional cross-correlation between the input and the kernel weights, and subsequently adding the scalar bias to the result.

Two-Dimensional Convolutional Layer Code Implementation

A practical illustration of a convolutional layer is a Waldo detector, which scans across an image to locate the character Waldo. The layer extracts small, localized windows from the input image and weights the pixel intensities using a specifically trained convolution filter, denoted as $$\mathsf{V}$$. The objective is to learn a filter such that locations in the image with the highest 'waldoness'—meaning they closely match the target pattern—result in peak activation values in the corresponding spatial locations of the hidden representation.

Waldo Detector Convolution Example

Edge detection is a fundamental image processing application of convolutional layers. It involves locating the boundaries of an object within an image by finding the precise locations of pixel changes. By performing a cross-correlation or convolution operation with a specifically constructed filter matrix, or kernel, the layer can successfully detect distinct kinds of boundaries, such as vertical or horizontal edges.

Object Edge Detection Using Convolution

The fundamental computation of a convolutional layer—typically implemented as a cross-correlation operation—is highly local, meaning that each output element depends only on a small, contiguous region of the input. This structural locality allows for significant hardware optimization, as chip designers can prioritize fast computation units over large memory capacity or bandwidth when designing accelerators for convolutions. While this specialized hardware design might not be optimal for all types of algorithms, it provides the computational efficiency necessary for affordable and ubiquitous computer vision applications.

Locality and Hardware Optimization of Convolutions

In modern convolutional neural network architectures, it is essential to utilize multiple channels at each layer. As data propagates deeper into the network, the channel dimension is typically increased. This architectural design explicitly trades off spatial resolution, which is usually downsampled, for greater channel depth, allowing the network to capture increasingly complex and jointly optimized feature representations.

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