The advent of powerful and versatile deep learning frameworks in recent years has made it possible to implement convolution layers into a deep learning model an extremely simple task, often achievable in a single line of code. However, understanding convolutions, especially for the first time can often feel a bit unnerving, with terms like kernels, filters, channels and so … [Read more...] about Intuitively Understanding Convolutions for Deep Learning
Computer Vision
An In-Depth Guide To Generative Adversarial Networks (GANs)
Generative Adversarial Networks are a powerful class of neural networks with remarkable applications. They essentially consist of a system of two neural networks — the Generator and the Discriminator — dueling each other. Given a set of target samples, the Generator tries to produce samples that … [Read more...] about An In-Depth Guide To Generative Adversarial Networks (GANs)
A Simple Guide to Semantic Segmentation
Semantic Segmentation is the process of assigning a label to every pixel in the image. This is in stark contrast to classification, where a single label is assigned to the entire picture. Semantic segmentation treats multiple objects of the same class as a single entity. On the other hand, instance segmentation treats multiple objects of the same class as distinct individual … [Read more...] about A Simple Guide to Semantic Segmentation
Intuitively Understanding Variational Autoencoders
In contrast to the more standard uses of neural networks as regressors or classifiers, Variational Autoencoders (VAEs) are powerful generative models, now having applications as diverse as from generating fake human faces, to producing purely synthetic music. This post will explore what a VAE is, the intuition behind why it works so well, and its uses as a powerful … [Read more...] about Intuitively Understanding Variational Autoencoders
Mixture of Variational Autoencoders – a Fusion Between MoE and VAE
The Variational Autoencoder (VAE) is a paragon for neural networks that try to learn the shape of the input space. Once trained, the model can be used to generate new samples from the input space. If we have labels for our input data, it’s also possible to condition the generation process on the label. In the MNIST case, it means we can specify … [Read more...] about Mixture of Variational Autoencoders – a Fusion Between MoE and VAE
Variational Autoencoders Explained in Detail
In the previous post of this series I introduced the Variational Autoencoder (VAE) framework, and explained the theory behind it. In this post I’ll explain the VAE in more detail, or in other words – I’ll provide some code 🙂 After reading this post, you’ll understand the technical details needed to implement VAE. As a bonus point, I’ll show you how by … [Read more...] about Variational Autoencoders Explained in Detail
Variational Autoencoders Explained
Ever wondered how the Variational Autoencoder (VAE) model works? Do you want to know how VAE is able to generate new examples similar to the dataset it was trained on? After reading this post, you'll be equipped with the theoretical understanding of the inner workings of VAE, as well as being able to implement one yourself. In a future post I'll provide you with a working code … [Read more...] about Variational Autoencoders Explained
5 New Generative Adversarial Network (GAN) Architectures For Image Synthesis
AI image synthesis has made impressive progress since Generative Adversarial Networks (GANs) were introduced in 2014. GANs were originally only capable of generating small, blurry, black-and-white pictures, but now we can generate high-resolution, realistic and colorful pictures that you can hardly distinguish from real photographs. Here we have summarized for you 5 … [Read more...] about 5 New Generative Adversarial Network (GAN) Architectures For Image Synthesis
10 Cutting Edge Research Papers In Computer Vision & Image Generation
UPDATE: We've also summarized the top 2019 and top 2020 Computer Vision research papers. Ever since convolutional neural networks began outperforming humans in specific image recognition tasks, research in the field of computer vision has proceeded at breakneck pace. The basic architecture of CNNs (or ConvNets) was developed in the 1980s. Yann LeCun improved upon … [Read more...] about 10 Cutting Edge Research Papers In Computer Vision & Image Generation