This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the center of recent progress on artificial intelligence and are of growing importance in cognitive science and neuroscience. They have enormous successes but also clear limitations. There is also only partial understanding of their inner workings. It seems unlikely that Deep Nets in their current form will be the best long-term solution either for building general purpose intelligent machines or for understanding the mind/brain, but it is likely that many aspects of them will remain. At present Deep Nets do very well on specific types of visual tasks and on specific benchmarked datasets. But Deep Nets are much less general purpose, flexible, and adaptive than the human visual system. Moreover, methods like Deep Nets may run into fundamental difficulties when faced with the enormous complexity of natural images which can lead to a combinatorial explosion. To illustrate our main points, while keeping the references small, this paper is slightly biased towards work from our group.

中文翻译：

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深网：他们为视觉做了什么？

这是一篇关于 Deep Nets 在视觉方面的优势和劣势的意见书。它们处于人工智能近期进展的中心，并且在认知科学和神经科学中的重要性日益增加。他们取得了巨大的成功，但也有明显的局限性。对他们的内部运作也只有部分了解。目前形式的深网似乎不太可能成为构建通用智能机器或理解思想/大脑的最佳长期解决方案，但它们的许多方面很可能会保留下来。目前，Deep Nets 在特定类型的视觉任务和特定的基准数据集上做得非常好。但与人类视觉系统相比，深度网络的通用性、灵活性和适应性要差得多。而且，当面对自然图像的巨大复杂性时，像深网这样的方法可能会遇到基本的困难，这可能导致组合爆炸。为了说明我们的主要观点，同时保持参考文献较少，本文略微偏向于我们小组的工作。