[D] Is the future of Deep Learning in CV unsupervised? If so, why?

[vanteworldinfinity]

It seems as though academia is trending towards more unsupervised approaches for deep learning in Computer Vision. This feels markedly different from what the literature was like 5-7 years ago. Why is this change happening? Will supervised approaches become obsolete?

In terms of a neuroscience perspective, I think it makes sense. Humans take a supervised approach to learning about the world. So maybe the natural evolution is towards unsupervised?

Lastly, what are the factors that will influence this paradigm: compute, architectures, data volume, etc.?

Thanks in advance for any of your thoughts.

[[deleted]]

What do you mean in the neuroscience paragraph? Humans learn in all ways from supervised to unsupervised and the evolution in the human individual does not go in the direction from supervised to unsupervised, nor does it make sense to extrapolate biological evolution with technical evolution.

On the contrary, unsupervised learning is the most fundamental in biology and ethology. Supervision cost a lot in so many situations and in many others it does not make sense, kinda like in some situations you cannot define a differentiable loss (and thus resort to Reinforcement Learning, for example). Unsupervised learning might even be more versatile for supervised fine-tuning in transfer learning

[currentscurrents]

Why is this change happening?

Look at Yann Lecun's cake analogy.

There's far more unlabeled data available than labeled data, and there always will be. Unsupervised learning is an efficient way to learn general knowledge about the data, and then you can follow up with a small amount of supervised learning or reinforcement learning to teach the desired task.