al use this network - which has been trained to be extremely effective at object recognition - as a basis for trying to extract content and style representations from images. In 2014, the winner of the ImageNet challenge was a network created by the Visual Geometry Group (VGG) at Oxford University, achieving a classification error rate of only 7.0%. All winning architectures in recent years have been some form of convolutional neural network - with the most recent winners even being able to surpass human level performance! One of the most popular benchmarks for image classification algorithms today is the ImageNet Large Scale Visual Recognition Challenge - where teams compete to create algorithms which classify objects contained within millions images into one of 1,000 different categories. What’s more, these representations will be independent from each other, so we can use the content representation from one image and style representation from another to generate a brand new image. al show that if we take a convolutional neural network that has already been trained to recognise objects within images then that network will have developed some internal representations of the content and style contained within a given image. See my earlier blog post for a more detailed explanation of these networks. It works by detecting features at larger and larger scales within an image and using non-linear combinations of these feature detections to recognise objects. The most effective neural network architecture for performing object recognition within images is the convolutional neural network. In this post I’ll attempt to briefly summarise the main concepts from the paper and share some results I obtained from my own implementation of the algorithm in TensorFlow. al show that the task of transferring the style from one image to the content of another can be posed as an optimisation problem which can be solved through training a neural network. ![]() The first major step in this field was introduced in the paper A Neural Algorithm of Artistic Style in September 2015. It’s no surprise that neural networks are at the heart of this capability. Some of the photos look like actual works of art! In fact they are works of art - but the artist is no longer a human - it’s an algorithm! Here are a few examples from the Prisma Instagram feed: ![]() Having recently played with the new Prisma app I was amazed at how seamlessly it is able to apply the style of a particular painting to any image from my camera roll.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |