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Deep-learning algorithm predicts photos’ memorability at “near-human” levels

news.mit.edu

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Re: Deep-learning algorithm predicts photos’ memorability at “near-human” levels

#21

Post by svantana »

stkni wrote: post #20

Uploading a URL of white noise from Wikipedia gave it a high-memorability (0.82) with lots of areas of interest. Secondly uploading a purely white image [2] produced high areas of interest in the top corners and a mediumly interesting image (0.62). I thought the tests might reveal something useful, like the eye-tracking heat-maps of Jakob Nielsen [3] but I'm not convinced. [1] https://upload.wikimedia.org/wikipedia/c…

A machine-learned system is only as good as its training data, at best. In this case, it was trained on natural-looking images, so results on "unnatural" images will be unpredictable/random/wrong.

One way to fix this would be to provide "bug bounty"-style rewards for producing images that makes the system deviate significantly from mechanical turk workers performing the same task. I wouldn't be surprised to see google/fb etc starting such programs in the near future, as their ML systems reach maturity.

Re: Deep-learning algorithm predicts photos’ memorability at “near-human” levels

#22

Post by eli_gottlieb »

ioeu wrote: post #14

> While deep-learning has propelled much progress in object recognition and scene understanding, predicting human memory has often been viewed as a higher-level cognitive process that computer scientists will never be able to tackle Seriously? Has it seriously often been viewed like that?

The AI Effect: if a computer can do it, it must not be thinking.

The Reverse AI Effect: if it's thinking, then surely it can't be done by a computer.

Because Humans Are Special.

Re: Deep-learning algorithm predicts photos’ memorability at “near-human” levels

#23

Post by visarga »

ZeroGravitas wrote: post #4

The point they seem to be going out of their way to avoid is whether humans are any good at this task. Anyone know if they are?

They don't ask humans how memorable a photo is. They experiment on humans memory to measure it.

Re: Deep-learning algorithm predicts photos’ memorability at “near-human” levels

#24

Post by ZeroGravitas »

visarga wrote: post #23
ZeroGravitas wrote: post #4

The point they seem to be going out of their way to avoid is whether humans are any good at this task. Anyone know if they are?

They don't ask humans how memorable a photo is. They experiment on humans memory to measure it.

I was going to quote the bit from the article, but on re-reading it, it's possibly just mangled English in the article that's confused me.

Re: Deep-learning algorithm predicts photos’ memorability at “near-human” levels

#25

Post by ioeu »

florianletsch wrote: post #18
ioeu wrote: post #16

> For each image, the algorithm produces a heat map showing which parts of the image are most memorable. By emphasizing different regions, they can potentially increase the image’s memorability. Most memorable, according to human subjects subjective thought on the matter? > The team then pitted its algorithm against human subjects by having the model predicting how memorable a group of people would find a new never-b…

Not based on human subject's opinion but on measured performance: > The images had each received a “memorability score” based on the ability of human subjects to remember them in online experiments.

In retrospect that seems much simpler than what I proposed in my edit, thanks.
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