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I think you've missed the point slightly. This paper looks at two cases, one of which is the "two images, two embeddings, one operation" (eg siamese-style). The
by nmca 8y ago
I think you've missed the point slightly. This paper looks at two cases, one of which is the "two images, two embeddings, one operation" (eg siamese-style). They note that this works well. They then go on to note that the problem of "are these two components at aribtrary locations of an otherwise blank image the same or different" is not well solved. This cannot be addressed directly by Siamese nets (well, or at least kind-of-can't. Arguably relational networks are similar to taking pixel pairwise functions, and thus have some relation to Siamese nets.)
In any case, they directly exclude the case you mention.
- paulsutter 8y agoMy comment was on the article, which was flat out wrong, and thanks for pointing that out in the paper I had missed that. Their paper was still unimaginative. Trying a handful of vanilla networks doesn’t prove anything. The right framing would have been, “here’s an interesting case to work on” instead of pretending to have identified a basic limitation of neural networks. This is more like a research idea than research.
- nmca 8y agoAh, I missed that you were referring to the article. To be honest, I skipped it and went to the paper :) Apologies!