[zo7]

VGG has 16-19 layers, Inception has ~50 layers, and ResNet has 150 layers. All of which were the state of the art at one point in time over the last ~2-3 years. A more faithful comparison with CNNs would've used one of these models pre-trained on a much larger face dataset.

Oh, BTW I do ML consulting lol

[forgotpw1123]

I don't think it's anywhere near conclusive yet that more layers = better. It's pretty telling that the current state-of-the-art is combining a bunch of layers together in a pseudo random fashion. Nobody understands how these things work to the point that we can make a formula or equation to produce better CNN's, or even predict which models will be more effective to any accuracy. You think more layers is better, because the best models we have happen to have the most layers? Some deep understanding of concepts there.

[zo7]

I don't think I or the parent comment are necessarily suggesting that more layers are better, but are pointing out that the fact that they're only using 5 layers suggests that they're not using a state of the art architecture. You can't faithfully say "oh a CNN cannot model this relationship" when it wasn't a thorough evaluation. (especially given that they don't mention modern face recognition systems like DeepFace or FaceNet, which I'd be interested to see if there's any correlation between the embeddings they produce if a simple PCA model works so well)

Also don't be so dismissive, we have a strong enough empirical and intuitive understanding of CNNs that we're able to make thoughtful improvements over time. In fact the insight behind the ResNet paper was noticing that adding layers doesn't improve performance and that training error actually degrades as layers are added – the solution to this was to construct the network so that it learns residual mappings that only modify the input rather than completely transform it. The whole point of that paper was solving this degradation problem so they could use some ridiculously deep architecture like a 150-layer network to get better results.

[nl]

the current state-of-the-art is combining a bunch of layers together in a pseudo random fashion

Please don't say things like this. Neural network is nothing like a random process - people understand very well when to use what kind of layer, and when to add more layers.

It's generally pretty well accepted that more layers = better because it gives you better ability to deal with multi-dimensional relationships between features.

There are two reasons not to add layers:

1) If you are overfitting. In visual tasks this is fairly rare, and there are other better ways to combat this.

2) It's harder to train. Until ResNet came out, the idea of training 100+ layers was considered unapproachable. Even now, more layers make a much harder training task.