When training deep learning models, you typically need a large amount of training data to achieve high accuracy. But what happens when you have limited training data? How can you improve the model's performance without collecting more data? Are there any special techniques or methods you can use?
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Many AI researchers/developers are currently breaking their head on this issue. Because what it looks like will soon be the high-quality data to train AI: https://1e9.community/t/den-ki-firmen-wohl-bald-die-daten-aus/20172
But there are already some techniques that are currently being tested or even used in practice.
For example, images can be used twice during the training of image models. For example, by being additionally mirrored, using a different image section or slightly roughened for training. It works in a similar way in texts where already used texts are “formulated” by an AI … and thus suddenly become two from a text.
In particular in image models, the power can also be increased by improving the metadata of the images used. OpenAI did this, for example, during training of DALL-E 3. It let an image captioner analyze the images in the dataset and provide more detailed descriptions that describe not only the image content, but also aesthetics, style and mood in a very detailed way.
Here you can read: https://cdn.openai.com/papers/dall-e-3.pdf
In addition, special techniques are also used to make models learn better from existing data. The Datology AI researches, for example, the so-called curriculum learning, in which data from an AI are presented in such a way that the “learn content” builds on one another and the AI profits optimally. Class is to count instead of mass. Instead of 100 articles on a topic, for example, the 10 best articles on a topic should be enough to achieve a better learning outcome.
However, it is also being researched to optimize the architecture of the models themselves so that they learn more from the existing data. Because such large amounts of data are required, of course, also lies with the models themselves. And there are progress here too. The CM3Leon, developed by Meta, an AI for image generation, should not need more billions, but “only” millions of images as training content to deliver robust results.
Here you can find information: https://ai.meta.com/blog/generative-ai-text-images-cm3leon/