7 ms·
The interesting part (IMO) isn't that the AI classifies it as both a rabbit and a duck, but that the classification is dependent on the rotation of the picture.
by mikejb 8y ago
The interesting part (IMO) isn't that the AI classifies it as both a rabbit and a duck, but that the classification is dependent on the rotation of the picture.
I can somewhat understand how that happens, but I find it's in an interesting observation (rather than a criticism on the system, though the title is somewhat unclear and had me expect something else).
- tanilama 8y agoBut rotation does contain information. 6 and 9 can be considered as a case where rotation SHOULD change the classifier's output. AI does makes dumb predictions from time to time, but I my opinion, this isn't that strong a case. When it rotate upside down, it does look like a rabbit even to me. The more interesting 'failure' here to me, is that while the rotation is smooth, the prediction is not, instead it is flickering, which does raise some interesting questions about what the model's internal distribution surface looks like.
- mikejb 8y ago> AI does makes dumb predictions from time to time, but I my opinion, this isn't that strong a case. I don't think it's meant as an example of a 'dumb prediction'. I think it's an interesting example that inspires (me at least) to think about how this recognition and classification works, and what could cause this effect.
- devin 8y agoThank you for this comment. To me, duck v. rabbit on rotation just proves the system works as intended.
- piva00 8y agoExactly, I also see a duck and a rabbit depending on rotation.
- lifthrasiir 8y agoIn the reddit comment the author noted that the training set is frequently rotated to help the system generalize better (maybe that's why people is fascinated by this animation at the first place). Possible explanations thus include: the Vision API is not well trained in this regard, or it has (correctly) learned that some images are sensitive to the rotation.
- salty_biscuits 8y agoBut only with context right? It is completely ambiguous if the character is 6 or 9 without some other clue, like which way up the paper is, or from the 4 next to it (if you assume that an arbitrary rotation may have occurred). It is just a sign to me that doing some rotations as data augmentation is not good enough. Rotation invariance needs to be built into the architecture of the network (like translation invariance sort of gets in via max pooling). I think it should be giving a 50/50 classification of duck and rabbit at all rotations if it was working as expected.
- devin 8y agoHow often do you read upside down?
- salty_biscuits 8y agoAll the time, especially when I read to my kids.
- swixmix 8y agoYour kids might enjoy this cartoon. https://youtu.be/9-k5J4RxQdE https://youtu.be/9-k5J4RxQdE
- ozzyman700 8y agoevery meeting
- FakeComments 8y ago> I think it should be giving a 50/50 classification of duck and rabbit at all rotations if it was working as expected. This would likely give incorrect or worse results when, eg, classifying ducks and rabbits in wildlife photos — animals come in orientations, and you’ll do a better job classifying them in practice if you respect that. It’s also not the case that a human would classify it 50/50 in all rotations — it certainly looks more or less like one or the other as you rotate it. Humans are even program,Ed for that compromise: we see faces in one orientation much better than rotated 180 degrees.
- Sharlin 8y agoThe distinction is present in the training data. So why wouldn't the classifier use the information? A completely rotation-invariant algorithm would most likely be more difficult to create, and would generate worse classifications because orientation is meaningful information.