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Key sentence - "the correct choice of face space axes is critical for achieving a simple explanation of face cells’ responses." They did PCA over two sets of m
by iandanforth 9y ago
Key sentence - "the correct choice of face space axes is critical for achieving a simple explanation of face cells’ responses."
They did PCA over two sets of metrics, taking the top 25 components from each set and then combined that into a 50d space. Using these dimensions and measured responses to fit a model resulted in explaining 57% of variance in real cell firing rates. (Much better than other models including a 5 layer CNN).
This is pretty cool. I'd like to see a follow-up where the chosen dimensions were further refined using something a bit more iterative that an arbitrary PCA cutoff.
Also I really want to know what eye motion was present during each trial. This paper presents a very "instantaneous" recognition perspective and doesn't talk about integration over time or the impact of sequential perception of face components on recognition. (Eg an upside-down face is hard to recognize because your gaze has to move up from the eyes to see the mouth which is a sequence rarely encountered in the real world)
- gumby 9y ago> Also I really want to know what eye motion was present during each trial. Incredibly important point. Looking at the physiology of the eye and the early visual system all together it's not clear that we can see anything without motion: object motion or saccade (saccade appears to be fundamental). Start with Lettvin's famous frog's eye paper. More interestingly, it does appear that recognition of a 2D image may be a complex, abstract learned behavor, while 3D recognition is at its core innate. And that writing (well, drawing) preceded reading.
- stinos 9y agoSee the methods: as usual for visual fixattion experiments with monkeys, the subjects were trained to maintain fixation, fixation window being only a part of the image size: stimulus size spanned 5.7 degrees. The fixation spot size was 0.2 degrees in diameter and the fixation window was a square with the diameter of 2.5 degrees. 2.5 is relatively large in my experience, might be due to lots of noise on the signal, but anyway there won't be true saccades (well, there shouldn't be, parts of recorded data wehere with saccades outside of that fixation window should get rejected). Probably microsaccades were present, but those aren't mentioned.
- highd 9y agoA 5 layer CNN is absurdly shallow, so this isn't particularly surprising. I routinely work with 150+ layer CNNs - that's fairly standard practice if you want high-quality results.
- iandanforth 9y agoThey only generated 2000 images to work with so I'm not sure any cnn can be expected to do terribly well.
- highd 9y agoTrue - it would be interesting to use, say, the coding layer of an autoencoder trained on one of the many face datasets and then training only on the last few layers to fit their data.
- halflings 9y agoSince when are 150+ layer CNNs fairly standard? Inception-v3 has about 50 layers, and that's considered a lot (requires considerable processing power to train).
- highd 9y agoI would say since ResNet beat state of the art on ImageNet in 2015. It's not standard, but that's what state-of-art can take.
- forgotpw1123 9y agoThis comment is absurd. "Your CNN sucks, I know this because I work with better ones all the time. (insert metric that doesn't mean much)" Oh, BTW I do ML consulting lol
- zo7 9y agoVGG has 16-19 layers, Inception has ~50 layers, and ResNet has 150 layers. All of which were the state of the art at one point in time over the last ~2-3 years. A more faithful comparison with CNNs would've used one of these models pre-trained on a much larger face dataset. Oh, BTW I do ML consulting lol