3 ms·

It's all about knowing what tools to use. The examples used in the article (in some cases the objects were identical; sometimes they were the same but with one

• bonoboTP 8y ago
  This is a toy/artificial example to emphasize and isolate a specific problem with CNNs. The reasoning behind it is that other tasks, for which CNNs are used in practice, would benefit from an ability to do well on this kind of matching tasks.
  • mv4 8y ago
    I view this not as a problem with CNN, but a problem with researchers picking the wrong tools. I've witnessed this behavior at large orgs and startups alike. Taking a model or approach, and feeding it input it's not designed for, is not good science. It's much more effective and economical to build a simple hybrid which knows when to use CNNs vs other methods - and ensemble the results.
    • bonoboTP 8y ago
      This is more like fundamental research, not applied problem-solving, engineering-like research. When you know that you have a problem at hand, where template matching would be a better approach, you can use that. However, it could be that more complex tasks (where usually CNNs are used) for which it's not immediately obvious that this capability is required, would also benefit from this capability. Ensembles are a practical solution, but it's kind of ugly. Remember, this is research into limitations and capabilities, not about solving particular tasks. Once the issue is identified it can be tackled and the resulting model may be nicer and more elegant than an ensemble.