What’s really fascinating about the current state of the art in computer vision/deep learning is just how many of the top research papers basically say “We tried this, it works, we’re not really sure why”
Psychology researchers try to justify deep, compelling theories of human behavior with small experiments that are hard to reproduce.
Deep Learning experiments are comparatively easy to run and reproduce because the field has both a common set of performance benchmarks and a large community of people implementing cutting-edge ideas in common frameworks. The harder part is building useful theory on top of those experiments.
This article gives some evidence: [1], quoting:
> researchers were a bit perplexed when actress and director Kristen Stewart [2] appeared as an author on a machine learning paper.
[1] https://techcrunch.com/2017/01/19/kristen-stewart-co-authore...
Of course, you can do fancy things with neural networks without fully understanding how they work, or why they behave like they do. But my impression is that common applications, like image classification, are quite well-understood now.