Next: the improvement and strengthening phase

[arzina221]

Concretely, how does it work?
The model learns to discern content types from batches of freely available public data.

We provide it with basic rules and characteristics so that it can practice finding the best possible combination of these to distinguish vacation photos from document photos, invoice photos from ID photos, etc.

Here's how it works:

First: the initial analysis and training phase
To detect documents, our model observes pixels to produce a set of rules that will represent characteristics such as:

a white pixel rate that would exceed 80%
the absence of people in the image
regular sized margins
a square or rectangular shape marked in the photo
etc.
It is after this initial training that the algorithm is able to ignore private photos and spontaneously move towards photos likely to be documents in order to categorize them with labels later.

Determining and labeling files with the right labels is only half the battle. Indeed, the algorithm must then learn from the user who will necessarily modify labels so that they correspond more to his files. This is for example the case when a user corrects a large number of labels. This implicit user feedback , as it is called, is the morocco mobile database consideration of the user's actions to improve the relevance of the algorithm in order to no longer reproduce the same errors.

These steps are continually repeated to feed the algorithm, strengthen learning and gain relevance. By adjusting the weight and threshold of the characteristics, the AI ​​learns to better recognize certain files in order to directly affix the correct label to them. In the long run, it no longer makes mistakes.

An individual AI, isolated from other users
No overflow possible. For personal feedback, we plan for each user to be able to train “their own AI” in a way. No data can leak to another user. Your “honeymoon” label will remain your label. Same for a custom label like “out of the box”. No one but you will know about it.

[xylanth]

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