A machine learning engineer is developing a Convolutional Neural Network (CNN) to classify high-resolution medical images. After initial training, the model is found to be computationally expensive and overly sensitive to the exact position of features within the images. To address these issues, the engineer decides to insert a new type of layer immediately after the convolutional layers. Which layer type would be most effective for reducing the spatial dimensions of the feature maps and providing a degree of translational invariance?
The correct answer is the pooling layer. Pooling layers, such as max pooling or average pooling, are specifically designed to reduce the spatial dimensions of the feature maps, which decreases the number of parameters and the computational load. This process, also known as down-sampling, makes the network more computationally efficient. Additionally, pooling provides a degree of translational invariance, meaning the network becomes more robust to small shifts in the position of features because it summarizes feature presence over a region rather than noting its precise location.
A dropout layer is a regularization technique used to prevent overfitting by randomly setting a fraction of neuron activations to zero during training; it does not reduce spatial dimensions.
A batch normalization layer is used to stabilize and accelerate the training process by normalizing the inputs to a layer. It does not perform spatial down-sampling.
A flatten layer is used to convert the 2D feature maps into a 1D vector to be fed into a fully connected layer; it reshapes the data but does not perform the kind of down-sampling that reduces computational load or provides translational invariance in the same way as pooling.
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What is translational invariance in CNNs?
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Why can't dropout layers or batch normalization replace pooling layers?