Alphabet GAN: AI Generates English Letters!

First, you need to know what a GAN really is. Well here’s a brief description. Generative Adversarial Network is a combination of two models namely Generator and Discriminator. The Generator tries to produce fake data mimicking the original data. On the other hand, the Discriminator tries to tell if a given data is original or fake. Thanks to the adversarial setup, eventually, both models keep getting better at their tasks. Of course, there’s much more to understand about GANs. Please watch this video if you are curious…

How do GANs work?

In this article, I want to show you how to implement one such GAN. I’ll also mention a whole bunch of tips that will help you in training your first GAN. But, before jumping into the model let’s understand the dataset.

Dataset: A-Z Handwritten Alphabets

Here, I’m using an MNIST style dataset of handwritten English alphabets. A-Z dataset contains 372,450 characters from 26 classes. Each data sample is a greyscale image of an alphabet. Like the MNIST dataset, the dimension of each image is 28px*28px and represented as a _784 _(28*28) dimensional vector. Let’s visualize a few of them…

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100 random images from the EMNIST Letters dataset

Originally, the pixel values range between [0, 255] but we should normalize them before feeding to any machine learning model. Generally, we normalize the pixels between [0, 1] by dividing 255.0 but here we normalize them between [-1, 1]. This is because we will use the tanh (range of tanh =**[-1, 1]**) function later.

Now let’s build our GAN. I like to do it in 4 steps.

1. Build the Generator (G)

The generator is a neural network that takes a noise vector (100-dimensional) as input and outputs an image of a single English alphabet. As we are working with image data, it makes sense to use a Convolutional Neural Network. The idea is to increase the spatial dimensions of the input as it passes through different layers until it reaches the desired output shape (28px*28px). The first two layers of the network are Dense layers with ReLu activation. I’d highly recommend using BatchNormalization on the output of each layer.

Note: BatchNormalization makes the training converge faster. A lot faster.

Notice that the first Dense layer contains 1024 neurons and the second one contains 6272 neurons. After that comes the Reshape layer. The reshaping is important because we want to use convolution afterward and to apply convolution we need matrix-like entities rather than column/row vectors.

Note: To find the correct dimensions we need to think backward! First, determine the dimensions of the matrices (7*7) and how many (128) of them you want then multiply them to get the dimension (77128 = 6272) of the Dense layer.

Before applying convolution we will upsample the matrices. I’ve used (2, 2) upsampling that will increase the dimension from 7*7 to 14*14.

UpSampling is a kind of inverse function of Pooling.

After that, we have 2*2 convolution filters (64). Notice that I have initialized the weights of the kernels according to a Normal distribution. The activation for this layer is LeakyReLu. Then again we have an upsampling layer followed by a convolution layer. This time the UpSampling layer will output 28*28 dimensional matrices. The last convolution layer contains only 1 filter because we want only one channel for our grayscale image. The activation function here is tanh. This is the reason why we normalized the pixel values between [-1, 1].

Note: We could have avoided UpSampling layers by using transposed convolutions. Because they can also increase the matrix dimensions.

#programming #neural-networks #deep-learning #artificial-intelligence #machine-learning #deep learning

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Alphabet GAN: AI Generates English Letters!
Otho  Hagenes

Otho Hagenes


Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

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Sequential Image Generation with GANs

Generative Adversarial Networks (GANs) are very successful in generating very sharp and realistic images. This post briefly explains our image generation framework based on GANs to sequentially compose an image scene, breaking down the underlying problem into smaller ones. For an in-depth description, please see our publication: A Layer-Based Sequential Framework for Scene Generation with GANs.

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Fig. 1: The proposed image generation process. Given a semantic layout map, our model composes the scene step-by-step. The first row shows the input semantic map and images generated by state-of-the-art baselines.

What is Generative Adversarial Network (GAN)?

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Fig. 2: GAN framework.

A generative adversarial network (GAN) [1] is a class of machine learning frameworks. Two neural networks: (i) generator, and (ii) discriminator contest with each other in a game-theoretic scenario. The generator takes a random noise as an input and generates a fake sample. The discriminator attempts to distinguish between samples drawn from the training dataset (real samples e.g hand-written digit images) and samples produced by the generative model (fake samples). This game drives the discriminator to learn to correctly classify samples as real or fake. Simultaneously, the generator attempts to fool the classifier into believing its samples are real. At convergence, the generators samples are indistinguishable from training data. For more details, please see the original paper or this post. GANs can be used for image generation; they are able to learn to generate sharp and realistic image data.

Single-shot image generation limits user control over the generated elements

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Fig. 3: Different scaling of the foreground object.

The automatic image generation problem has been studied extensively in the GAN literature [2,3,4]. It has mostly been addressed as learning a mapping from a single source, e.g. noise or semantic map, to target, e.g. images of zebras. This formulation sets a major restriction on the ability to control scene elements individually. So for instance, it is difficult to change the appearance or shape of one zebra while keeping the rest of the image scene unaltered. Let’s look at Fig. 3. If we change the object size, the background is also changed even though the input noise is the same for each row.

Our approach: Layer-based Sequential Image Generation

Our main idea resembles how a landscape painter would first sketch out the overall structure and later embellish the scene gradually with other elements to populate the scene. For example, the painting could start with mountain ranges or rivers as background while trees and animals are added sequentially as foreground instances.

The main objective is broken down into two simpler sub-tasks. First, we generate the background canvas** x0** with the background generator Gbg conditioned on a noise. Second, we sequentially add foreground objects with the foreground generator Gfg to reach the final image xT, which contains the intended T foreground objects on the canvas (T is not fixed). Our model allows user control over the objects to generate, as well as, their category, their location, their shape, and their appearance.

#image-generation #generative-adversarial #deep-learning #gans #ai #deep learning

Murray  Beatty

Murray Beatty


This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

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This Week in AI - Issue #22 | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.Have fun!

Research Papers


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amelia jones


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