In this tutorial, we understand the Context-Cluster model from Adobe and show how to train your own model with a ready-to-use codebase on the Gradient Notebook.
In this blog post, we take a deeper look at the inversion techniques utilized by GANs to embed features and then manipulate them in the latent space,
In this article we look at the Style Space in StyleGAN models - a way for us to understand and identify style channels that control both local semantic regions and specific attributes defined by positive samples
Introducing the new YOLOv8 Web UI - image labeling, training, and inference in a single GUI.
This article reviews the development of the influential StyleGAN model throughout its development history.
In this tutorial, we understand Data2Vec model from Meta AI and show how to train your own model with a ready-to-use codebase on the Gradient Notebook.
Boosting the performance and generalization of models by ensembling multiple neural network models.
In this article, we explored a broad overview of epistemic uncertainty in deep learning classifiers, and develop intuition about how an ensemble of models can be used to detect its presence for a particular image instance.
In this article, we took a brief look at uncertainties in deep learning. Thereafter, we took a more keen look at aleatoric uncertainty and how convolutional autoencoder can help to screen out-of-sample images for classification tasks.
