Nilay Nilesh Jaltare

Varational Autoencoders & Diffusion

Exploring Image Reconstruction and Colorization using Autoencoders

A variant of autoencoders designed to remove noise from input data. Key Concept: The encoder is trained to remove noise by reconstructing the original clean data from noisy input. Application: Image denoising, data preprocessing. Autoencoders are a type of neural network designed to learn efficient coding of input data. Variational Autoencoders (VAEs) are a type of generative model in machine learning, particularly used for unsupervised learning. They are a variant of traditional autoencoders with a probabilistic twist, allowing them to generate new data points similar to the input data. Convolutional Autoencoder for Image Colorization - Harnessing the power of deep learning, this presentation explores a convolutional autoencoder model that can convert grayscale images into vibrant, full-color counterparts. By leveraging the model's ability to learn rich features and patterns, we can breathe new life into monochrome photos, unlocking a world of visual possibilities. Auto encoders architectures capable of learning efficient representations of data by compressing inputs into a lower-dimensional latent space and reconstructing them back. They find wide application in tasks like data denoising, dimensionality reduction, image colorization and image generation.