My comment here wasnt aimed at the problem of understanding and improving DNNs; rather, of generalisation and 'comprehension' proper.
3d image reconstruction is trivially possible, with the right data and with the right assumptions encoded into the right algorithm.
My target of attack here is the equivocation that 'compression is comprehension'.
In my view comprehension isnt about 'generalization via compressed archetypes' as in ordinary NNs.
And it isnt about 3d reconstruction given the relevant modification to data and approach.
Rather generalization is abduction. It is the ability to, via a single instance, form
law-like provisional universalising models which explain your environment. This process will lead to a 'compressed', 'representation', but nothing like the sense in which compression is here used.
It is this that intuitively is naively assumed of these systems. They do not, and cannot, abduct. Abduction isnt a statistical process; involving at the very least, counterfactual reasoning and hypothetical action.
This is the problem that such equivocations miss (ie. that c = c).
And in my view this is an engineering challenge; not something to be specified for a universal computer.
The relevant capacities missing arent better means of compression.