What is Knowledge Distillation?
Deep neural networks have grown in popularity for a variety of applications ranging from recognising items in images using object detection models to creating language using GPT models. Deep learning models, on the other hand, are frequently huge and computationally costly, making them challenging to deploy on resource-constrained devices like mobile phones or embedded systems. Knowledge distillation solves this issue by condensing a huge, complicated neural network into a smaller, simpler one while retaining its performance.
The Virtuous Cycle of Synthetic Data in AI-powered Products
The virtuous cycle of data needs to be expanded by new modalities including synthetic data to further enhance product development and customers willingness to share more data.
A Comprehensive Strategy For Computer Vision By Combining Data-Centric And Model-Based Approaches With High Quality Synthetic Datasets
In this article, you'll discover how to think about your machine learning models from a data-centric standpoint, stressing the relevance and value of data in the AI models creation process.
What is Synthetic Data?
Synthetic data is one of the most useful data-driven AI approaches
Accelerating Model Development and AI Training with Synthetic Data, SKY ENGINE AI Platform for 5G RAN
One way to bridge the data gap and accelerate model training is by using synthetic data instead of real data for training. SKY ENGINE AI provides a platform to move deep learning to virtual reality. It is possible to generate synthetic data using simulations where the synthetic images come with the annotation that can be used directly in training AI models.