This cartoon filter still has the same issues as previous attempts, which is:
- omitting borders that are semantically important but have a low color gradient
- not collapsing small areas into lines
For the first issue, there's an example image of a picnic on white background. A human cartoonist would most likely draw a full outline around the white spoon, because it is important for conveying the type of object that this is supposed to be. With this algorithm, the spoon gets partially merged with the white background and without the reference photo I would have a hard time identifying it as a spoon.
For the second issue, kook at the photo of the Asian girl with patterned skirt. A human cartoonist would most likely observe the regular grid pattern and replace it with thin lines, thereby communicating that all of it is one same thing. This algorithm, on the other hand, treats each tile of the pattern individually, thereby making it look more like a crystal or crumbled foil.
I personally also prefer white-box algorithms, but there's no denying that creating a cartoon requires a lot of prior knowledge about which features to retain as important and which features to abstract away. As such, I see the real challenge in somehow producing good saliency training data for millions of images. I mean ideally you would want the 5 year video stream plus eye tracking data of a baby starting to grow up...
Something similar is going on with the photo of the merlion statue. The entire body is scaled, and a cartoonist would definitely represent that. But (because of lighting?), the algorithm renders the tail smooth instead of scaled.
As for input data to models, my intuition is that they would be tainted by the copyright of the input images. It's just that nobody has a bot for scanning AI models for their photographs, so you don't see a lot of litigation or DMCA takedown requests here. It's easy when someone just uploads your photo to their website. It's hard when the photo contributes some weights to a neural network.
My main takeaway is that copyright is very imperfect. It doesn't allow for any unsolicited enhancements of someone else's work.
I think human faces and bokeh could use some improvement.
The former seems especially tricky; showing or hiding certain lines might change the perceived emotion and result in a different image.
If you make an imaging effect, please make sure it works at all scales. And I'm also looking at you bokeh-folks ;)
I'm as liberal as they come but — is this the kind of thing we're going to be doing now? Really?
Anyways, you didn't look hard enough. The forth person on the page is African American (the first three appear to be: one Asian person and two Indigenous persons).
Well I think this is incorrect. Generally speaking, a black-box exposes an interface that you can use, and a white-box is something you can internally modify.
One is not necessarily better than the other. Using a white-box approach can create tight-coupling between components in a system as you could be relying on internal mechanisms, whereas a black-box approach enforces boundaries in your system which is generally good. Also, it's often better to test systems with a black-box mentality to ensure security, resilience etc...
It's not inherently representing good/bad, however modern terms like, opaque/transparent might be more accurate and less controversial I suppose.
Between day and night, between black and white
There is more, there is more than gray
Between the question and the answer
There's the silence of the sea
Between the cradle and the grave
There is the someone that is me
Between yesterday and tomorrow
There is more, there is more than a day
White box is not better, it just defeats the mystery/freedom of the abstraction.
That said, given that we describe races as white and black, I'm happy to eventually pick up a new convention if it makes folks uncomfortable. I say eventually because the current trend is to consider most of these shallow accommodations as 'performative', and I have no interest in being involved in any action that could be construed as being done for social credit where there is little or no actual value. If i felt or had evidence that it actually did help it would be a different story.
The terms “black box” and “white box” just don’t do a very good job of illuminating the concepts they represent to people new to the field — white doesn’t mean transparent.
No, those terms greatly predate any kind of ML.
Another term is crystal box.