Using Active Learning to Improve your Machine Learning Models
Machine Learning Reality Check In the Machine Learning World or broadly in the AI Universe, the colonists such as Data Scientists, Machine Learning Engineers, Deep Learning Specialist are coached towards a belief i.e. “More Training Data Means Highly Accurate Production Model“. Which to some extent is unavoidably true but predominately it’s also a fact, that […]
Continue Reading
Understanding and improving Image to Image Translation Pix2PixHD
Introduction Photo-realistic image rendering using standard graphics techniques requires realistic simulation of geometry and light. The algorithms which we use currently for the task are effective but expensive. If we were able to render photo-realistic images using a model learned from data, we could turn the process of graphics rendering into a model learning and […]
Continue Reading
Understanding StyleGAN for Image Generation using Deep Learning
Introduction Images produced by generative methods have been improving lately. Most of the recent generative algorithms have made use of generative networks that are trained using a discriminator network as their adversary. Generative Adversarial Networks (GANs) or generators, in other words, are a relatively new concept in the field of computer vision. Their aim is […]
Continue ReadingState-Of-The-Art Text to Image Generation using DALL-E
DALL-E – Creating images from text Code – openai/DALL-E: PyTorch package for the discrete VAE used for DALL·E. Paper – https://arxiv.org/pdf/2102.12092.pdf What is DALL-E? On 5th January 21, OpenAI unveiled their novel text to image generation model, DALL-E. This model is capable of generating various types of images from textual descriptions. A humongous 12 […]
Continue Reading
Training Image Classification 8x Faster With NFNet
Introduction: Anyone who has deployed a neural network on production knows that deploying a network is easy but making sure that it stays updated as new user data flows is a harder task. It involves keep training the network with new incoming data frequently and in such a case being able to train faster is […]
Continue Reading
