Add speed and simplicity to your Machine Learning workflow today Get startedTalk to an expert Since Ian Goodfellow gives machines the gift of imagination by creating a powerful AI concept GANs, researchers start to improve the generation images both when it comes to fidelity and diversity. Yet much of the work focused on improving the discriminator, and the generators continue to operate as black boxes until researchers from NVIDIA AI released StyleGAN from the paper A Style-Based Generator Architecture for Generative Adversarial Networks, which is based on ProGAN from the paper Progressive ..read more

Bring this project to life Run on Gradient Context Cluster Convolutional Neural Networks and Vision based Transformer models (ViT) are widely spread techniques to process images and generate intelligent predictions. The ability of the model to generate predictions solely depends on the way it processes the image. CNNs consider an image as well-arranged pixels and extract local features using the convolution operation by filters in a sliding window fashion. On the other side, Vision Transformer (ViT) descended from NLP research and thus treats an image as a sequence of patches and will extr ..read more

Generative models are machine learning algorithms that can create new data similar to existing data. Image editing is a growing use of generative models; it entails creating new images or modifying existing ones. We’ll start by defining a few important terms: GAN Inversion → Given an input image $x$, we infer a latent code w, which is used to reconstruct $x$ as accurately as possible when forwarded through the generator $G$. Latent Space Manipulation → For a given latent code $w$, we infer a new latent code, $w’$, such that the synthesized image $G(w’)$ portrays a semantically meaningful edit ..read more

Add speed and simplicity to your Machine Learning workflow today Get startedTalk to an expert Deep learning models are known to be ‘black boxes’ because they contain a degree of randomness which they use to predict ‘magical’ results that make sense to us humans. Image generation models are deep learning models that are trained on thousands if not millions of images today and it is impossible to detail all the information they learn from the training data which is then used to generate photorealistic images. What we do know for sure is that input images from the training data are encoded int ..read more

Bring this project to life Run on Gradient Historically, object detection was one of the first deep learning technologies that became viable for consumer use. This was largely thanks to the incredible work of Joseph Redmon and his research team to develop the first generations of You Only Look Once (YOLO) models. There powerful framework enabled people with more casual computing setups to get started with YOLO object detection, and begin to integrate the AI framework into their existing applications. This tradition was continued with the devlopment of YOLOv5 and YOLOv8 at Ultralytics. They ..read more

Bring this project to life Run on Gradient Model ensembling is a common practice in machine learning to improve the performance and generalizability of models.  It can be simply described as the technique of combining multiple models in diverse ways to improve performance on a single problem. The major merits of model ensembling include its ability to improve the performance, robustness and generalization of machine learning models on unseen data. Tree-based algorithms are particularly known to perform better for certain tasks due to their ability to utilize the ensembling of multiple ..read more

Bring this project to life Run on Gradient One of the issues which plague deep learning models is the fact that they often do not know what they do not know. That being the case models might need an added layer of protection against data classes which they have not been exposed to during training. In this article, we will look at one of such methods in detail. # article dependencies import torch import torch.nn as nn import torch.nn.functional as F import torchvision import torchvision.transforms as transforms import torchvision.datasets as Datasets from torch.utils.data import Dataset ..read more