A Gentle Introduction to Deep Learning for Face Recognition

Last Updated on July 5, 2019 Face recognition is the problem of identifying and verifying people in a photograph by their face. It is a task that is trivially performed by humans, even under varying light and when faces are changed by age or obstructed with accessories and facial hair. Nevertheless, it is remained a challenging computer vision problem for decades until recently. Deep learning methods are able to leverage very large datasets of faces and learn rich and compact […]

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How to Perform Face Detection with Deep Learning

Last Updated on August 24, 2020 Face detection is a computer vision problem that involves finding faces in photos. It is a trivial problem for humans to solve and has been solved reasonably well by classical feature-based techniques, such as the cascade classifier. More recently deep learning methods have achieved state-of-the-art results on standard benchmark face detection datasets. One example is the Multi-task Cascade Convolutional Neural Network, or MTCNN for short. In this tutorial, you will discover how to perform […]

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How to Perform Face Recognition With VGGFace2 in Keras

Last Updated on August 24, 2020 Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. Recently, deep learning convolutional neural networks have surpassed classical methods and are achieving state-of-the-art results on standard face recognition datasets. One example of a state-of-the-art model is the VGGFace and VGGFace2 model developed by researchers at the Visual Geometry Group at Oxford. Although the model can be challenging to implement and resource intensive to […]

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How to Develop a Face Recognition System Using FaceNet in Keras

Last Updated on August 24, 2020 Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. FaceNet is a face recognition system developed in 2015 by researchers at Google that achieved then state-of-the-art results on a range of face recognition benchmark datasets. The FaceNet system can be used broadly thanks to multiple third-party open source implementations of the model and the availability of pre-trained models. The FaceNet system can be […]

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One-Shot Learning for Face Recognition

Last Updated on June 11, 2020 One-shot learning is a classification task where one, or a few, examples are used to classify many new examples in the future. This characterizes tasks seen in the field of face recognition, such as face identification and face verification, where people must be classified correctly with different facial expressions, lighting conditions, accessories, and hairstyles given one or a few template photos. Modern face recognition systems approach the problem of one-shot learning via face recognition […]

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Best Resources for Getting Started With GANs

Last Updated on July 12, 2019 Generative Adversarial Networks, or GANs, are a type of deep learning technique for generative modeling. GANs are the techniques behind the startlingly photorealistic generation of human faces, as well as impressive image translation tasks such as photo colorization, face de-aging, super-resolution, and more. It can be very challenging to get started with GANs. This is both because the field is very young, starting with the first paper in 2014, and because of the vast […]

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18 Impressive Applications of Generative Adversarial Networks (GANs)

Last Updated on July 12, 2019 A Generative Adversarial Network, or GAN, is a type of neural network architecture for generative modeling. Generative modeling involves using a model to generate new examples that plausibly come from an existing distribution of samples, such as generating new photographs that are similar but specifically different from a dataset of existing photographs. A GAN is a generative model that is trained using two neural network models. One model is called the “generator” or “generative […]

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A Gentle Introduction to Generative Adversarial Networks (GANs)

Last Updated on July 19, 2019 Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks. Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset. GANs are a […]

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Tips for Training Stable Generative Adversarial Networks

Last Updated on September 12, 2019 The Empirical Heuristics, Tips, and Tricks That You Need to Know to Train Stable Generative Adversarial Networks (GANs). Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods such as deep convolutional neural networks. Although the results generated by GANs can be remarkable, it can be challenging to train a stable model. The reason is that the training process is inherently unstable, resulting in the simultaneous dynamic […]

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How to Implement GAN Hacks in Keras to Train Stable Models

Last Updated on July 12, 2019 Generative Adversarial Networks, or GANs, are challenging to train. This is because the architecture involves both a generator and a discriminator model that compete in a zero-sum game. It means that improvements to one model come at the cost of a degrading of performance in the other model. The result is a very unstable training process that can often lead to failure, e.g. a generator that generates the same image all the time or […]

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