Is there much in the way of datasets of things that are recognizable but triggering incorrect something-or-other (what is it exactly? Semantic sensations?)
When we as humans see a face in a cookie or something we quite often spend some time gazing at it noting what it is about the the arrangement of features that triggers the pareidolia. I have wondered if that urge to contemplate the similarity difference is an instinctive response to gather more data for future accuracy.
I guess in a similar vein there would be merit in collecting a data set of all of those quirks that generative AI produces to provide a point of comparison. Notably when most image generators produce artifacts it's because the local image generation is accurate but at odds with the global image. So fingers are beside fingers accurately but there are too many, or a swirl near some hair becomes hair but doesn't actually connect to the top of the head.
> Is there much in the way of datasets of things that are recognizable but triggering incorrect something-or-other (what is it exactly? Semantic sensations?)
There are some datasets which collect 'hard' examples. One I love for the title alone is "When does dough become a bagel? Analyzing the remaining mistakes on ImageNet" https://arxiv.org/abs/2205.04596#google , Vasudevan et al 2022. The errors can be pretty interesting: https://arxiv.org/pdf/2205.04596#page=18 You can often see why a model might make a mistake and that you would too. (Like that first one of a swing - I would think it's some sort of large "tripod" too because I have no idea if I've ever seen a swing like that.)
You are looking at it from your own rationalist point of view. Most humans will apply supernatural, magical, religious or/and spiritual meanings to it. This applies to faces in surfaces as well voices in wind or patterns in noise.
They have a method for finding animal doppelgängers, perhaps same approach could work on human faces. There is a demo next to the paragraph about it, quoted below.
> We can even use the deep feature representation of our trained animal and pareidolia detector to compute animal-pareidolia doppelgangers
Is there much in the way of datasets of things that are recognizable but triggering incorrect something-or-other (what is it exactly? Semantic sensations?)
When we as humans see a face in a cookie or something we quite often spend some time gazing at it noting what it is about the the arrangement of features that triggers the pareidolia. I have wondered if that urge to contemplate the similarity difference is an instinctive response to gather more data for future accuracy.
I guess in a similar vein there would be merit in collecting a data set of all of those quirks that generative AI produces to provide a point of comparison. Notably when most image generators produce artifacts it's because the local image generation is accurate but at odds with the global image. So fingers are beside fingers accurately but there are too many, or a swirl near some hair becomes hair but doesn't actually connect to the top of the head.
> Is there much in the way of datasets of things that are recognizable but triggering incorrect something-or-other (what is it exactly? Semantic sensations?)
There are some datasets which collect 'hard' examples. One I love for the title alone is "When does dough become a bagel? Analyzing the remaining mistakes on ImageNet" https://arxiv.org/abs/2205.04596#google , Vasudevan et al 2022. The errors can be pretty interesting: https://arxiv.org/pdf/2205.04596#page=18 You can often see why a model might make a mistake and that you would too. (Like that first one of a swing - I would think it's some sort of large "tripod" too because I have no idea if I've ever seen a swing like that.)
The very idea that some objects have a single, correct label is fundamentally a simplification.
https://en.wikipedia.org/wiki/Adversarial_machine_learning#A...
You are looking at it from your own rationalist point of view. Most humans will apply supernatural, magical, religious or/and spiritual meanings to it. This applies to faces in surfaces as well voices in wind or patterns in noise.
Can someone fine-tune this to find the face of Jesus in things?
They have a method for finding animal doppelgängers, perhaps same approach could work on human faces. There is a demo next to the paragraph about it, quoted below.
> We can even use the deep feature representation of our trained animal and pareidolia detector to compute animal-pareidolia doppelgangers