Is there any difference between Conv1d(in, out, kernel_size=1) and Conv2d(in, out, kernel_size=1)?

Question

Are these two Conv operator the same for serial data?

I want to know how to select from these two Conv operator

Answer 1

Conv1d is a convolutional layer that operates on sequential data with one spatial dimension, such as text or time-series data. It applies a 1-dimensional convolution to the input tensor, sliding a kernel of size kernel_size along the input sequence, and producing an output tensor with one spatial dimension.

On the other hand, Conv2d is a convolutional layer that operates on image data with two spatial dimensions. It applies a 2-dimensional convolution to the input tensor, sliding a kernel of size kernel_size along the height and width dimensions of the input image, and producing an output tensor with two spatial dimensions.

Therefore, while both layers perform convolution, the difference lies in the number of spatial dimensions of the input data they operate on, with Conv1d operating on one spatial dimension and Conv2d operating on two spatial dimensions.

It's important to choose the appropriate convolutional layer based on the nature of the input data, to ensure that the convolution operation is applied correctly and produces meaningful results.

When kernel_size=1, both Conv1d and Conv2d layers apply a filter of size 1 to the input tensor. In this case, both layers perform what is called a "1x1 convolution".

However, despite using the same filter size, Conv1d and Conv2d are still different layers that operate on different types of data. Conv1d operates on sequential data with one spatial dimension, while Conv2d operates on image data with two spatial dimensions. Even when kernel_size=1, Conv1d and Conv2d layers are not equivalent and should be used appropriately depending on the nature of the input data.

It's worth noting that kernel_size=1 is often used in Conv2d layers for dimensionality reduction or feature aggregation, while maintaining the spatial structure of the data. This technique is often used in convolutional neural network architectures, such as the ResNet, to reduce the number of feature maps or channels without affecting the spatial dimensions of the input.