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:
CNNs typically have more layers (at least five), while autoencoders can function with fewer layers, even one, although deeper architectures tend to perform better.
Both are feedforward networks, but autoencoders may also utilize recirculation during training.
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!
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:
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