Convolution Neural Networks (CNNs) are powerful tools that can process any data that looks like an image (matrices) and find important information from it, however, in standard CNNs, every channel is given the same importance. This is what Squeeze and Excite Network improves, it dynamically gives importance to certain channels only (an attention mechanism for channel correlation). Standard CNNs abstract and extract features of an image with initial layers learning about edges and texture and final layers extracting shapes of objects, performed by convolving learnable filters or kernels, howeve ..read more

Neural style transfer is a technique that allows us to merge two images, taking style from one image and content from another image, resulting in a new and unique image. For example, one could transform their painting into an artwork that resembles the work of artists like Picasso or Van Gogh. Here is how this technique works, at that start you have three images, a pixelated image, the content image, and a style image, the Machine Learning model transforms the pixelated image into a new image that maintains recognizable features from the content and style image. Neural Style Transfer (NST) has ..read more

Imagine if the solutions to our most complex problems were already perfected in nature. This is the essence of biomimicry — drawing inspiration from natural processes and systems to fuel human innovation. In this blog, we explore how mimicking nature leads to cutting-edge advancements in AI vision. We’ll see how biological concepts inspire the development of computer vision technologies.   The Role of Biomimicry in Human Innovation In broad terms, biomimicry is the discipline of solving human problems using means inspired by natural phenomena. Thus, it’s the practice of developing solutio ..read more

The Gartner Hype Cycle on AI presents the pace of AI development nowadays and in the near future. It emphasizes the opportunities for innovation and the potential risks. Companies can use the hype cycle to adopt new technologies or avoid adopting AI too early, or waiting too long. Gartner Hype Cycle on AI includes 5 phases: Innovation Trigger – an occurrence of a technology or a product launch, that people start talking about. Peak of Expectations – when product usage increases, but there’s still more hype than proof that the innovation will deliver the company’s need. Disillusionment – when ..read more

Since the introduction of GANs (Generative Adversarial Networks) by Goodfellow and his colleagues in 2014, they have revolutionized generative models and have been useful in various fields for image generation, creating synthetic faces and data. Moreover, beyond image generation, GANs have been used extensively in a variety of tasks such as image-to-image translation (using CycleGAN), super-resolution, text-to-image synthesis, drug discovery, and protein folding. Image-to-image translation is an area of computer vision that deals with transforming one image to another form while maintaining ce ..read more

Stable Diffusion (SD) is a Generative AI model that uses latent diffusion to generate stunning images. This deep learning model can generate high-quality images from text descriptions, other images, and even more capabilities, revolutionizing the way artists and creators approach image creation. Despite its powerful capabilities, learning to use Stable Diffusion effectively can have a steep learning curve. In this comprehensive guide, we’ll break down the complexities. We’ll cover everything from the fundamentals of how it works to advanced techniques for fine-tuning the model to create unique ..read more

NVIDIA in 2018 came out with a breakthrough Model- StyleGAN, which amazed the world for its ability to generate ultra-realistic and high-quality images. Before StyleGAN, NVIDIA did come up with the predecessor- ProGAN, however, this model could not fine-control the features of images generated. StyleGAN is GAN (Generative Adversarial Network), a Deep Learning (DL) model, that has been around for some time, developed by a team of researchers including Ian Goodfellow in 2014. Since the development of GANs, the world saw several models introduced every year that got nearer to generating real imag ..read more

Artificial intelligence (AI) is not a monolithic field, idea, or area of study. Instead, researchers are working on a spectrum of AI technologies with different capabilities, applications, and “levels of intelligence.” Today, we consider the following three broad categories when discussing the capability and scope of AI systems: Artificial Narrow Intelligence (ANI), Artificial General Intelligence ( AGI), and Artificial Super Intelligence (ASI). About us: Viso Suite is the only end-to-end computer vision infrastructure. By implementing Viso Suite, ML teams can build, deploy and scale real ..read more

On June 18 (2024), NVIDIA dethroned Microsoft in the market capital and became “the most valuable company in the world.” The market capital of this American multinational tech corporation hit $3.34 trillion by overtaking Microsoft’s $3.32 trillion valuation. NVIDIA’s meteoric rise is attributable to the company’s dominance in a red-hot sector: Artificial Intelligence (AI). It has now become a leading supplier of specialized GPUs and chips for AI systems worldwide. Industry experts often call these AI processors “the new gold or oil in the tech industry.” In this article, we will discuss: NVID ..read more

In today’s digital world, Artificial Intelligence (AI) and Machine learning (ML) models are used everywhere, from face detection in electronic devices to real-time language translation. Efficient, quick, and cost-effective learning processes are crucial for scaling these models. Transfer Learning is a key technique implemented by researchers and ML scientists to enhance efficiency and reduce costs in Deep learning and Natural Language Processing. In this blog, we’ll explore the concept of transfer learning, how it technically works, and provide a step-by-step guide to implementing it in Python ..read more