#DataAugmentation

[cizotech]

𝐖𝐡𝐚𝐭 𝐢𝐟 𝐲𝐨𝐮 𝐝𝐨𝐧’𝐭 𝐡𝐚𝐯𝐞 𝐞𝐧𝐨𝐮𝐠𝐡 𝐃𝐀𝐓𝐀?

Let’s say you're building an AI to recommend personalized meal plans. But your dataset? A few hundred users. That’s like training a chef with just 10 recipes—nowhere near enough to master the craft.

This is where data augmentation comes in.

Instead of waiting years for more real-world data, we generate it. Using techniques like SMART (Self-Supervised Multi-Task Augmentation), we expand the dataset by creating synthetic examples that mimic real user behaviors.

GANs (Generative Adversarial Networks) take it a step further—producing entirely new, lifelike data points that AI can learn from.

For a fitness app, we once simulated thousands of heart rate variations during workouts. That way, the AI didn’t just recognize patterns from a small dataset—it learned to handle real-world edge cases, like sudden spikes or inconsistent readings.

𝐌𝐨𝐫𝐞 𝐝𝐚𝐭𝐚 → 𝐁𝐞𝐭𝐭𝐞𝐫 𝐠𝐞𝐧𝐞𝐫𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 → 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐀𝐈.

And this isn’t just about fitness. Whether it's medical imaging, predictive maintenance, or fraud detection, augmentation is the key to making AI faster, more accurate, and truly adaptive.

If your AI model is starving for data, it’s time to feed it smarter, not just more.

𝐖𝐡𝐚𝐭’𝐬 𝐭𝐡𝐞 𝐨𝐧𝐞 𝐚𝐫𝐞𝐚 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐀𝐈 𝐬𝐭𝐢𝐥𝐥 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞𝐬 𝐭𝐨 𝐠𝐞𝐭 𝐫𝐢𝐠𝐡𝐭?

Let’s build AI that adapts, not just reacts. Contact CIZO today! 💡

[sentivium]

Introducing GR00T-Mimic and GR00T-Gen: using both Graphics 1.0 & Graphics 2.0 to multiply your robot datasets by 1,000,000x.

Robotics is right in the thick of Moravec's paradox: things that are easy for humans turn out to be incredibly hard for machines. We are crushing the Moravec's paradox, one token at a time.

> Graphics 1.0: Isaac simulators with manually written, GPU-accelerated physics and rendering equations.

> Graphics 2.0: big neural nets (Cosmos) that repaint the pixels from sim textures to real, given an open-ended prompt.

[volansystechnologies]

The quantity and variation of data are important for the performance of most ML models (e.g. deep learning neural network models). Thus, the training of the neural network models requires a very large dataset. Then only it can achieve the accuracy expected in the production-ready model.

[innovaturelabs]

Data augmentation is something that thrust in the deep learning computer vision tasks. As it has the ability to generate more data without actually creating new data is giving immense help for deep learning in the domains where we cannot access the big data. 

For more information go to Innovature’s Python/Django page 

[sovitdc]

So, in this article, we will see different image augmentations that we can apply while carrying out deep learning training. We will take a practical approach with:

PyTorch image augmentation techniques for deep learning.

Using albumentations library for deep learning image augmentation.

[factorsofchange-blog]

Microelectrodes attached to nerves in amputated arm renew wearer's sense of touch

POSTED BY: Kiefer Shanks