In standard AI projects, sufficient data is a must for the assumption of successful neural network training. In various applications and domains, manual acquisition of large amount of data is difficult, expensive and sometimes even impossible, for example when collecting data for anomaly detection. All of those problems, and now include the manual annotation of thousands of training and validation examples.
However, artificial intelligence methods provide us with options to reduce these problems. One possibility is the automatic creation of datasets by generating synthetic data with real quality. GANs, DALL-E, Midjourney, and other generative models are currently experiencing huge success thanks to high-quality results that can be used not only for art works, but also for augmentation of the datasets necessary for training other expert models.
In standard AI projects, sufficient data is a must for the assumption of successful neural network training. In various applications and domains, manual acquisition of large amount of data is difficult, expensive and sometimes even impossible, for example when collecting data for anomaly detection. All of those problems, and now include the manual annotation of thousands of training and validation examples.
However, artificial intelligence methods provide us with options to reduce these problems. One possibility is the automatic creation of datasets by generating synthetic data with real quality. GANs, DALL-E, Midjourney, and other generative models are currently experiencing huge success thanks to high-quality results that can be used not only for art works, but also for augmentation of the datasets necessary for training other expert models.
If you missed the livestream, you can watch the recording here:
https://www.youtube.com/watch?v=7Xq3tyQoRXs&ab_channel=BETTER_AIMeetup
#AI #betterAImeetup #betterai #artificialintelligence #cognexa #conference #bratislava #GAN #neuralnetwork
