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PixelCNN is a deep learning architecture – or bundle of architectures – designed to generate highly realistic-looking images. To use it, no reverse-engineering of arXiv papers or search for reference implementations is required: TensorFlow Probability and its R wrapper, tfprobability, now include a PixelCNN distribution that can be used to train a straightforwardly-defined neural network […]
Easy PixelCNN with tfprobability Read More »
Generative adversarial networks (GANs) are a popular deep learning approach to generating new entities (often but not always images). We show how to code them using Keras and TensorFlow eager execution.
Generating images with Keras and TensorFlow eager execution Read More »
Continuing our series on combining Keras with TensorFlow eager execution, we show how to implement neural style transfer in a straightforward way. Based on this easy-to-adapt example, you can easily perform style transfer on your own images.
Neural style transfer with eager execution and Keras Read More »
Image captioning is a challenging task at intersection of vision and language. Here, we demonstrate using Keras and eager execution to incorporate an attention mechanism that allows the network to concentrate on image features relevant to the current state of text generation.
Attention-based Image Captioning with Keras Read More »
Conditional GANs (cGANs) may be used to generate one type of object based on another – e.g., a map based on a photo, or a color video based on black-and-white. Here, we show how to implement the pix2pix approach with Keras and eager execution.
Image-to-image translation with pix2pix Read More »
Like GANs, variational autoencoders (VAEs) are often used to generate images. However, VAEs add an additional promise: namely, to model an underlying latent space. Here, we first look at a typical implementation that maximizes the evidence lower bound. Then, we compare it to one of the more recent competitors, MMD-VAE, from the Info-VAE (information maximizing
Representation learning with MMD-VAE Read More »
Object detection (the act of classifying and localizing multiple objects in a scene) is one of the more difficult, but very relevant in practice deep learning tasks. We’ll build up to it in several posts. Here we start with the simpler tasks of naming and locating a single object.
Naming and locating objects in images Read More »
In deep learning, there is no obvious way of obtaining uncertainty estimates. In 2016, Gal and Ghahramani proposed a method that is both theoretically grounded and practical: use dropout at test time. In this post, we introduce a refined version of this method (Gal et al. 2017) that has the network itself learn how uncertain
You sure? A Bayesian approach to obtaining uncertainty estimates from neural networks Read More »
As shown in a previous post, naming and locating a single object in an image is a task that may be approached in a straightforward way. This is not the same with general object detection, though – naming and locating several objects at once, with no prior information about how many objects are supposed to
Concepts in object detection Read More »
Having to train an image-classification model using very little data is a common situation, in this article we review three techniques for tackling this problem including feature extraction and fine tuning from a pretrained network.
Image Classification on Small Datasets with Keras Read More »
