In the context of autoencoders, what is the significance of the "bottleneck" layer?
- The bottleneck layer reduces model complexity
- The bottleneck layer enhances training speed
- The bottleneck layer compresses input data
- The bottleneck layer adds noise to data
The "bottleneck" layer in an autoencoder serves as the compression layer, reducing input data to a lower-dimensional representation. This compression is essential for capturing essential features in a compact representation, facilitating feature extraction and denoising.
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