I am currently try to use auto-encoders based deep learning for my classification problem. I have found some very nice examples from Matlab website auto-encoders example.

1. In this example, they used 2 auto-encoders layers and a softmax layer (see the figure below). Will the deep learning community consider this structure as a 'deep' or still a 'shallow' network? I am asking because it seems to me that in recent literatures, there are many deep networks contains >10 layers.

2. May I ask if in general 'deeper' network will produce better classification? Or if there are any tricks to improve the classification performance for auto-encoders?

Thanks a lot. A.

I am currently try to use auto-encoders based deep learning for my classification problem. I have found some very nice examples from Matlab website auto-encoders example.

1. In this example, they used 2 auto-encoders layers and a softmax layer (see the figure below). Will the deep learning community consider this structure as a 'deep' or still a 'shallow' network? I am asking because it seems to me that in recent literatures, there are many deep networks contains >10 layers.

2. May I ask if in general 'deeper' network will produce better classification? Or if there are any tricks to improve the classification performance for auto-encoders?