4 ms·
All the things you've said, bar temporal integration, are also present in CNNs in some way. Training data is "augmented" with different levels of luminosity to
by halflings 9y ago
All the things you've said, bar temporal integration, are also present in CNNs in some way.
Training data is "augmented" with different levels of luminosity to cancel the affect that has on classification, pooling layers give some translation invariance (and avg pooling is a type of blur), and resolution is also very limited in most models (and gets smaller as you get deeper). And it still fails!
- raverbashing 9y agoAh yes, but I mean there are two types of temporal integration One is the short-timed "persistence of vision" one The other is observing a scene multiple times by slightly different angles and positions If some weird arrangement causes an illusion, changing the position slightly usually fixes that Also, human data is noisy, I guess augmentation strategies might want to consider that as well
- scottlegrand2 9y agoSo here's an idea to test that. Take an image and find one of these adversary generating pixels. Now take that same corresponding pixel and modify it in each of the other images in exactly the same way that generated the first adversary. I would not be surprised if it is not an adversary generator across all images. I would expect each of those images has a different gradient.