What is the role of bottleneck layer in UNet architecture?

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The vary famous UNet model has three parts: 1 Encoder, 2 Decoder, and 3 Bottleneck.
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As far as I understand …
Encoder does the contextual feature extraction.
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Decoder does the localization of features

but I do not understand the role of bottleneck… I want to know that is there any specific role of bottleneck layer in the UNet model ?? Anyone please make me understand. Please correct me for encoder and decoder as well if I am wrong…

… … Thank you everyone

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Bottlenecks in Neural Networks are a way to force the model to learn a compression of the input data.
The idea is that this compressed view should only contain the “useful” information to be able to reconstruct the input (or segmentation map).

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Thanks Sir :slightly_smiling_face::v: Your answer will help me to understand this more clearly