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It is easy to introduce different measure which will make deep learning fail and which will improve the state of the art and humanness significantly. They are:
by nshm 10y ago
It is easy to introduce different measure which will make deep learning fail and which will improve the state of the art and humanness significantly. They are: 1) Learning from small amount of examples, make training database very small with 3-4 pictures by class. 2) Learning across classes. Train on evening pictures and learn to recognize daylight. Train on direct sunlight and learn to recognize on side light. 3) Learning of entities. Train on entity in white and learn to recognize a combined picture. Deep learning community intentionally do not consider these tasks, they prefer to increase the training data size to the limit only huge corporation could afford doing a research.
- PeterisP 10y agoDeep learning is currently the best way to solve tasks such as your #1 example - there are great results with learning to identify classes from even a single labeled example; and the proper (only?) way to do this is to use basic feature-extraction layers made by showing the system a bunch of unlabelled images.