Many machine learning systems look at some kind of complicated input (say, an image) and produce a simple output (a label like, "cat"). By contrast, the goal of a generative model is something like the opposite: take a small piece of input—perhaps a few random numbers—and produce a complex output, like an image of a realistic-looking face. A generative adversarial network (GAN) is an especially effective type of generative model, introduced only a few years ago, which has been a subject of intense interest in the machine learning community.
ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. Currently we have an average of over five hundred images per node. We hope ImageNet will become a useful resource for researchers, educators, students and all of you who share our passion for pictures.Click here to learn more about ImageNet, Click here to join the ImageNet mailing list.
In this codelab, you will learn how to build a simple "teachable machine", a custom image classifier that you will train on the fly in the browser using TensorFlow.js, a powerful and flexible machine learning library for Javascript. You will first load and run a popular pre-trained model called MobileNet for image classification in the browser. You will then use a technique called "transfer learning", which bootstraps our training with the pre-trained MobileNet model and customizes it to train for your application.
Using JavaScript and frameworks like Tensorflow.js is a great way to get started and learn more about machine learning. In this article, Charlie Gerard covers the three main features currently available using Tensorflow.js and sheds light onto the limits of using machine learning in the frontend.
This tutorial demonstrates how to generate images of handwritten digits using a Deep Convolutional Generative Adversarial Network (DCGAN). The code is written using the Keras Sequential API with a tf.GradientTape training loop.
shaoanlu/faceswap-GAN: A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.
GAN Lab: Play with Generative Adversarial Networks in Your Browser!
ImageNet
TensorFlow.js Transfer Learning Image Classifier
Machine Learning For Front-End Developers With Tensorflow.js — Smashing Magazine
face-api.js
Deep Convolutional Generative Adversarial Network | TensorFlow Core
DALL·E: Creating Images from Text