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After I collect a dataset I save duplicate entries with the text reversed. More data is good data. Except now my machine learning robot is dyslexic.

why_only_15 8y ago
Of course you're joking here, but for images people do reverse the image to enhance the output of their models and this has shown to be beneficial. There are other augmentation you can do as well. In total they're called "Synthetic Data".
- superconformist 8y ago
  You see, I'm speaking only the truth, merely referring to synthetic data. And to think that they're downvoting me, poor fools.
- gnulinux 8y ago
  This is not surprising because if image A is an apple, its mirror image A' is also an apple. Adding A' to your dataset is just plain ol' regularization. It is meant to lower bias, to prevent overfitting.
  - Topolomancer 8y ago
    It's not surprising, but highly necessary because you might otherwise learn too little about structural features, and too much about unnecessary things such as the background colour of some objects.
  - jzwinck 8y ago
    It will also make the flag of Côte d’Ivoire be perceived as that of Ireland.
- paradoxparalax 8y ago
  The other day I was thinking that if I create a black cube in a simulation with white background, and generate a number of PNG images of the cube in different positions, The adjusted weights I get in my network when it classifies with success in further simulations(applying the learned weights to classify now, instead of learning and adjusting the weights), Can also be used to classify a real life black dice on a real life white table, if the camera is well positioned and the light is right. In this case, the learning would "generalize", from the simulation to the real life. Maybe this simulated data used to adjust the weights could also be called "synthetic data" or "simulated data" or just "artificially generated data".
Topolomancer 8y ago
You may joking about it, but ML researchers use all kinds of strategies like this one in order to perform what they call _data augmentation_. In particular for computer vision tasks, creating a perturbed variant of your input images (slightly warped, flipped, mirror, what have you...) can do wonders for the generalization performance.

why_only_15 8y ago

Of course you're joking here, but for images people do reverse the image to enhance the output of their models and this has shown to be beneficial. There are other augmentation you can do as well. In total they're called "Synthetic Data".

superconformist 8y ago
You see, I'm speaking only the truth, merely referring to synthetic data. And to think that they're downvoting me, poor fools.
gnulinux 8y ago
This is not surprising because if image A is an apple, its mirror image A' is also an apple. Adding A' to your dataset is just plain ol' regularization. It is meant to lower bias, to prevent overfitting.
- Topolomancer 8y ago
  It's not surprising, but highly necessary because you might otherwise learn too little about structural features, and too much about unnecessary things such as the background colour of some objects.
- jzwinck 8y ago
  It will also make the flag of Côte d’Ivoire be perceived as that of Ireland.
paradoxparalax 8y ago
The other day I was thinking that if I create a black cube in a simulation with white background, and generate a number of PNG images of the cube in different positions, The adjusted weights I get in my network when it classifies with success in further simulations(applying the learned weights to classify now, instead of learning and adjusting the weights), Can also be used to classify a real life black dice on a real life white table, if the camera is well positioned and the light is right. In this case, the learning would "generalize", from the simulation to the real life. Maybe this simulated data used to adjust the weights could also be called "synthetic data" or "simulated data" or just "artificially generated data".

Topolomancer 8y ago

You may joking about it, but ML researchers use all kinds of strategies like this one in order to perform what they call _data augmentation_. In particular for computer vision tasks, creating a perturbed variant of your input images (slightly warped, flipped, mirror, what have you...) can do wonders for the generalization performance.