Archives for autoencoders

07 Jan

All You Need to Know About Masked Autoencoders

masking is a process of hiding information of the data from the models. autoencoders can be used with masked data to make the process robust and resilient.

17 Nov

A Tutorial on Sequential Machine Learning

Vijaysinh Lendave AI

Machine learning models that input or output data sequences are known as sequence models. Text streams, audio clips, video clips, time-series data, and other types of sequential data are examples of sequential data.

25 Sep

A Guide to Different Types of Noises and Image Denoising Methods

Vijaysinh Lendave AI

Here we will discuss how Convolutional Neural Networks and Autoencoders are used to denoise an image.

25 May

Getting Started With Image Generation Using TensorFlow Keras

Rajkumar Lakshmanamoorthy autoencoders

Image Generation is one of the most curious applications in Computer Vision. Variational Autoencoders and GANs are the preferred base models

The post Getting Started With Image Generation Using TensorFlow Keras appeared first on Analytics India Magazine.

09 Mar

Guide to ODE2VAE For Long-Term Motion Prediction

Rajkumar Lakshmanamoorthy Autoencoder in PyTorch

ODE2VAE, a representation learning model, achieves state-of-the-art performance in long-term motion prediction and imputation tasks

The post Guide to ODE2VAE For Long-Term Motion Prediction appeared first on Analytics India Magazine.

09 Jul

How to Implement Convolutional Autoencoder in PyTorch with CUDA

Dr. Vaibhav Kumar Autoencoder in PyTorch

In this article, we will define a Convolutional Autoencoder in PyTorch and train it on the CIFAR-10 dataset in the CUDA environment to create reconstructed images.

The post How to Implement Convolutional Autoencoder in PyTorch with CUDA appeared first on Analytics India Magazine.

08 Jul

Hands-On Guide to Implement Deep Autoencoder in PyTorch for Image Reconstruction

Dr. Vaibhav Kumar Autoencoder in PyTorch

In this article, we will demonstrate the implementation of a Deep Autoencoder in PyTorch for reconstructing images. This deep learning model will be trained on the MNIST handwritten digits and it will reconstruct the digit images after learning the representation of the input images.

The post Hands-On Guide to Implement Deep Autoencoder in PyTorch for Image Reconstruction appeared first on Analytics India Magazine.

17 Mar

The Recreation Of Fingerprints Images With Convolutional Autoencoders

Rohit Chatterjee autoencoders

With security being regarded as the most important factor in any organisation and is also a matter of grave concern, biometric authentication systems are the most reliable when it comes to verifying a person’s identity. In the article, we will cover in brief how Aditya Sharma reconstructed fingerprints using convolutional autoencoders. Biometrics are usually classified…

The post The Recreation Of Fingerprints Images With Convolutional Autoencoders appeared first on Analytics India Magazine.

23 Jun

How Feature Extraction Can Be Improved With Denoising

Ram Sagar autoencoders

The life cycle of a machine learning models involves a training phase, where a typical data scientist develops a model with good prediction based on historical data and features extracted from the data at hand. This model is then put into production with the hope that it would continue to have similar predictive performance during…

The post How Feature Extraction Can Be Improved With Denoising appeared first on Analytics India Magazine.