Deep Learning: Science Fiction or Reality?

 

Customers are expected to use the right tool for each application and provide training images representing all different product cases. (Bild: Adaptive Vision - Future Processing Sp. z o.o.)

Image 4 | Customers are expected to use the right tool for each application and provide training images representing all different product cases. (Pictures: Adaptive Vision – Future Processing Sp. z o.o.)

20 to 50 training images

What is very important is that we can have a very limited set of tools to cover a wide variety of industrial applications without the need to design custom neural network architectures for each particular case. There is no need to learn TensorFlow to successfully apply deep learning. Customers are just expected to use the right tool for each application and provide training images representing all different product cases. Contrary to a popular belief, it is also not required to provide hundreds or thousands of object samples. The most advanced software manufacturers provide highly sophisticated solutions that can be trained with typically 20 to 50 training images within five to ten minutes on a modern GPU platform. This is possible due to techniques such as usage of pre-trained networks, advanced preprocessing and artificial generation of raw training data from just a few initial samples. Details of these solutions are technical secrets of each of the supplier that required quite a lot of effort to bring it into reality, but for a regular user this are just out-of-the-shelf solutions that can be instantly applied in a huge variety of applications.

Conclusion

Everything I have described is not just research topics or ideas for next generations. These are ready products that are getting into practical use in an unprecedented pace. A major challenge now appears to be in our ability to change our mindsets in a fast way to keep up with that progress. This may be particularly difficult to more traditional industries where major revolutions took decades in the past, while today those who are not progressive enough may become obsolete in a matter of two or three years.

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