Do convolutions “flatten images”?

Answer: No, convolutions do not flatten images; they are used for feature extraction by applying filters to detect patterns within local regions of the image.

Convolutional operations in neural networks are primarily employed for feature extraction and spatial hierarchies within images. Here’s a more detailed explanation:

1. Convolution Operation: Convolution involves sliding a small filter (also known as a kernel) over the input image in a systematic way. At each step, the filter performs element-wise multiplication with the input pixels it covers, and the results are summed to produce a single value in the output feature map. This process is repeated across the entire image, generating a new set of values that form the convolved output.
2. Feature Extraction: Convolutional layers are effective in extracting hierarchical features from images. Lower layers tend to capture basic features like edges and textures, while higher layers aggregate these features to represent more complex patterns or objects.
3. No Flattening: Flattening, in the context of neural networks, refers to the process of converting a multi-dimensional array (like an image) into a one-dimensional array. This is often done before feeding the data into fully connected layers. Convolutional layers, on the other hand, maintain the spatial structure of the input. They operate on local regions and preserve the spatial relationships between pixels.
4. Pooling Operations: While not flattening, pooling operations are commonly used in conjunction with convolutions to downsample feature maps. Pooling reduces the spatial dimensions of the input and focuses on the most salient features. This downsampling helps in reducing computational complexity and promoting translation invariance.

Conclusion:

In summary, convolutions in neural networks do not flatten images; instead, they preserve the spatial structure of the input data, allowing for effective feature extraction and maintaining the hierarchical representation of features within images. The flattening typically occurs in subsequent layers, such as fully connected layers, which process the extracted features for final classification or regression tasks.