Search results for "embedding"

A Gentle Introduction to Dimensionality Reduction for Machine Learning

Introduction to Dimensionality Reduction for Machine Learning

The number of input variables or features for a dataset is referred to as its dimensionality. Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. More input features often make a predictive modeling task more challenging to model, more generally referred to as the curse of dimensionality. High-dimensionality statistics […]

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Examples of Class Leakage in an Image Generated by Partially Trained BigGAN

A Gentle Introduction to BigGAN the Big Generative Adversarial Network

Generative Adversarial Networks, or GANs, are perhaps the most effective generative model for image synthesis. Nevertheless, they are typically restricted to generating small images and the training process remains fragile, dependent upon specific augmentations and hyperparameters in order to achieve good results. The BigGAN is an approach to pull together a suite of recent best […]

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Example of 100 Photos of Sneakers Generated by an AC-GAN

How to Develop an Auxiliary Classifier GAN (AC-GAN) From Scratch with Keras

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images. The conditional generative adversarial network, or cGAN for short, is a type of GAN that involves the conditional generation of images by a generator model. Image generation can be conditional on a class label, […]

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Example of 100 Generated items of Clothing using a Conditional GAN.

How to Develop a Conditional GAN (cGAN) From Scratch

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images. Although GAN models are capable of generating new random plausible examples for a given dataset, there is no way to control the types of images that are generated other than trying to figure out […]

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Photo of a dog at the beach.

How to Develop a Deep Learning Photo Caption Generator from Scratch

Develop a Deep Learning Model to Automatically Describe Photographs in Python with Keras, Step-by-Step. Caption generation is a challenging artificial intelligence problem where a textual description must be generated for a given photograph. It requires both methods from computer vision to understand the content of the image and a language model from the field of […]

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One-Shot Learning with Siamese Networks, Contrastive, and Triplet Loss for Face Recognition

One-Shot Learning for Face Recognition

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 […]

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Detected Face of Jerry Seinfeld, Correctly Identified by the SVM Classifier

How to Develop a Face Recognition System Using FaceNet in Keras

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 […]

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