Why Hypersynthetic Data is the Future of Vision AI and Machine Learning
Hypersynthetic data is redefining vision AI training by using n-dimensional feature spaces to design custom datasets that go beyond conventional synthetic datasets. By leveraging advanced simulation engines, physics-based rendering, and feature-space modeling, SKY ENGINE AI enables highly scalable, accurate, and bias-free AI training. Learn how our Synthetic Data Cloud empowers organizations to build future-proof AI systems.
Using Learning Curves to Analyse Machine Learning Model Performance
Learning curves are a common diagnostic tool in machine learning for algorithms that learn progressively from a training dataset. After each update during training, the model may be tested on the training dataset and a hold out validation dataset, and graphs of the measured performance can be constructed to display learning curves.
What is Mask R-CNN?
Mask R-CNN, or Mask Region-based Convolutional Neural Network, is an extension of the Faster R-CNN object detection method, which is used in computer vision for both object recognition and instance segmentation.
Autoencoders in Computer Vision
An autoencoder is a type of artificial neural network that is used to learn data encodings unsupervised. The autoencoder must examine the input and create a function capable of transforming a specific instance of that data into a meaningful representation.
Zero-shot learning in Computer Vision/Vision AI
Zero-shot learning (ZSL) is a machine learning technique that enables a model to categorise items from previously unseen classes without getting any explicit training for those classes.
How to Split Dataset in Machine Learning?
To prevent overfitting and to correctly evaluate your model, divide your data into train, validation, and test batches
What is a neural network?
The development of neural networks is an active subject of study, as academics and businesses attempt to find more efficient ways to handle complicated problems using machine learning.
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.