Question 7Which of the following are true about the inception Network? (Check all that apply)

The Inception Network, also known as GoogLeNet, is a convolutional neural network architecture that was introduced by Google for image classification tasks. Here are some key truths about the Inception Network:

1. Simplicity in Depth: Inception networks can effectively increase the depth of the network without significantly increasing computational complexity.

2. Inception Modules: The architecture is built using Inception modules, which apply multiple convolutions with different kernel sizes simultaneously and concatenate the results.

3. Dimensionality Reduction: It employs 1x1 convolutions before expensive 3x3 and 5x5 convolutions, which helps in reducing dimensionality and computational cost.

4. Auxiliary Classifiers: To combat the vanishing gradient problem, Inception Networks use auxiliary classifiers during training, which provide additional gradients to the earlier layers.

5. State-of-the-art Performance: The Inception Network achieved state-of-the-art performance in the ILSVRC 2014 competition.

When assessing which statements are true about the Inception Network, you should look for these characteristics among the options given in your question. Be sure to verify each statement based on your understanding of the architecture.