[Lecture2-2][1009] Convolutional Autoencoder vs. (Deep) CNN

[mosouka]

In the lecture, we discussed the use of convolutional layers in autoencoders. I previously studied deep convolutional neural networks (CNNs), and I'm trying to understand the distinctions between the two. Based on my research, I noted that:

1. CNNs typically have more layers (at least five), while autoencoders can function with fewer layers, even one, although deeper architectures tend to perform better.
2. Both are feedforward networks, but autoencoders may also utilize recirculation during training.
3. Autoencoders have a broader range of applications beyond image processing, while CNNs are generally used for image-related tasks.

Could you clarify these differences or provide more insights into how autoencoders and CNNs differ in their structure and applications?

Thank you in advance!

Cosima Balzer

[jleem99]

I think you can simply think of it this way:

• CNNs are just neural networks with convolutional layers, and they can learn meaningful visual features through the overall network design.
• Autoencoders are networks with two components: an encoder and a decoder, and they aim to learn efficient encoding in the latent space by minimizing the reconstruction error.

While the encoder and decoder in autoencoders are often built with fully connected layers, they can also be formed with convolutional layers like in the "convolutional" autoencoder, that is, CNN being part of autoencoder architecture.

[yjyoo3312]

@mosouka @jleem99 Thanks for the question and the comment:)

jleem's answers are correct. To add to that:

• Autoencoders can also have multiple layers. For example, the Autoencoder in Noh et al., an early approach utilizing ConvTranspose2D, employs a VGG16 CNN classification network as the encoder.
• For specific tasks, such as in diffusion-based generation methods, models like UNet (Autoencoder + skip connections) are used repeatedly (we'll cover this later). However, in most cases, Autoencoders follow a single feedforward process.

Thus, CNNs and Autoencoders are not inherently in the same category. While we can design an Autoencoder using a CNN architecture, convolutional layers are widely used across various applications in computer vision.