Same-different problems strain convolutional neural networks
Same-different problems strain convolutional neural networks Ricci et al., arXiv 2018
Since we’ve been looking at the idea of adding structured representations and relational reasoning to deep learning systems, I thought it would be interesting to finish off the week with an example of a problem that seems to require it: detecting whether objects in a scene are the same or different.
This image containing a flute was correctly classified by a CNN trained on millions of photographs. On ImageNet the network even surpassed the accuracy of a human observer.

This image contains two shapes that are the same, a relationship that is immediately obvious to a human observer. “Yet, the CNN failed to learn this relation even after seeing millions of training examples.”

The above is an example of a same-different (SD) visual relation problem (output whether the objects in the scene are the same, or different). Spatial relation (SR) problems ask whether objects follow a certain spatial relation, e.g. in a line, horizontally stacked, vertically stacked, and so on. For example:

The synthetic visual reasoning test (SVRT) contains a collection of 23 binary classification problems along these lines. In each case opposing classes differ Continue reading
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