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How to Implement Pix2Pix GAN Models From Scratch With Keras

#artificialintelligence

The Pix2Pix GAN is a generator model for performing image-to-image translation trained on paired examples. For example, the model can be used to translate images of daytime to nighttime, or from sketches of products like shoes to photographs of products. The benefit of the Pix2Pix model is that compared to other GANs for conditional image generation, it is relatively simple and capable of generating large high-quality images across a variety of image translation tasks. The model is very impressive but has an architecture that appears somewhat complicated to implement for beginners. In this tutorial, you will discover how to implement the Pix2Pix GAN architecture from scratch using the Keras deep learning framework. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code. How to Implement Pix2Pix GAN Models From Scratch With Keras Photo by Ray in Manila, some rights reserved.


Clustering appearance and shape by learning jigsaws

Neural Information Processing Systems

Patch-based appearance models are used in a wide range of computer vision applications. Tolearn such models it has previously been necessary to specify a suitable set of patch sizes and shapes by hand. In the jigsaw model presented here, the shape, size and appearance of patches are learned automatically from the repeated structures in a set of training images. By learning such irregularly shaped'jigsaw pieces', we are able to discover both the shape and the appearance of object parts without supervision. When applied to face images, for example, the learned jigsaw pieces are surprisingly strongly associated with face parts of different shapes and scales such as eyes, noses, eyebrows and cheeks, to name a few. We conclude that learning the shape of the patch not only improves the accuracy of appearance-based part detection but also allows for shape-based part detection. This enables parts of similar appearance but different shapes to be distinguished; forexample, while foreheads and cheeks are both skin colored, they have markedly different shapes.


Clustering appearance and shape by learning jigsaws

Neural Information Processing Systems

Patch-based appearance models are used in a wide range of computer vision applications. To learn such models it has previously been necessary to specify a suitable set of patch sizes and shapes by hand. In the jigsaw model presented here, the shape, size and appearance of patches are learned automatically from the repeated structures in a set of training images. By learning such irregularly shaped'jigsaw pieces', we are able to discover both the shape and the appearance of object parts without supervision. When applied to face images, for example, the learned jigsaw pieces are surprisingly strongly associated with face parts of different shapes and scales such as eyes, noses, eyebrows and cheeks, to name a few. We conclude that learning the shape of the patch not only improves the accuracy of appearance-based part detection but also allows for shape-based part detection. This enables parts of similar appearance but different shapes to be distinguished; for example, while foreheads and cheeks are both skin colored, they have markedly different shapes.


Building an Image Classifier using TensorFlow – Data Driven Investor – Medium

#artificialintelligence

We are going to use this existing model and build our own on top of it. This approach brings with it numerous advantages. For instance, it will save us a lot of time, some of the parameters that the Inception has already learned can be reused and we can still build a pretty accurate classifier with far less training data. This process of reusing pre-trained models on different but related tasks is known as Transfer Learning in the world of Deep Learning. First step is to download the training images for your classifier.


Emergence of Object Segmentation in Perturbed Generative Models

Neural Information Processing Systems

We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation that the location of object segments can be perturbed locally relative to a given background without affecting the realism of a scene. Our approach is to first train a generative model of a layered scene. The layered representation consists of a background image, a foreground image and the mask of the foreground. A composite image is then obtained by overlaying the masked foreground image onto the background.