Concept: Skip connections, depth, bottlenecks

Advanced Convolutional Neural Network (CNN) architectures often incorporate mechanisms such as skip connections, increased depth, and bottleneck layers to improve training stability and performance. These concepts are key to understanding powerful models like ResNet and Inception-ResNet.

Illustration of skip connections and bottleneck blocks in deep CNNs

Here is a simple implementation of a residual block with a bottleneck design using TensorFlow/Keras:

from tensorflow.keras import layers, Model

def residual_bottleneck_block(inputs, filters):
    x = layers.Conv2D(filters // 4, (1, 1), activation='relu')(inputs)
    x = layers.Conv2D(filters // 4, (3, 3), padding='same', activation='relu')(x)
    x = layers.Conv2D(filters, (1, 1))(x)

    shortcut = layers.Conv2D(filters, (1, 1))(inputs)  # Match dimensions
    output = layers.Add()([x, shortcut])
    output = layers.Activation('relu')(output)
    return output

This block first reduces the feature dimensions (bottleneck), processes them, then restores the dimensions and adds the input back via a skip connection.