Nov 11, 2023 · When it comes to simple autoencoders, the choice of loss function plays a pivotal role in shaping the outcome of our model. To comprehend this better, let’s explore two fundamental types of loss...

Aug 16, 2024 · This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection. An autoencoder is a special type of neural network that is trained to copy its input to its output.

May 14, 2016 · To build an autoencoder, you need three things: an encoding function, a decoding function, and a distance function between the amount of information loss between the compressed representation of your data and the decompressed representation (i.e. a …

Mar 1, 2025 · Autoencoders aim to minimize reconstruction error which is the difference between the input and the reconstructed output. They use loss functions such as Mean Squared Error (MSE) or Binary Cross-Entropy (BCE) and optimize …

Dec 14, 2023 · Autoencoders are a special type of unsupervised feedforward neural network (no labels needed!). The main application of Autoencoders is to accurately capture the key aspects of the provided data to provide a compressed version of the input data, generate realistic synthetic data, or flag anomalies.

May 26, 2020 · There is of course not a magic thing that you can do to instantly reduce the loss as it is very problem specific, but here is a couple tricks that I could suggest: Reduce mini-batch size. Having a smaller batch size will make the gradient more noisy when it's back-propagating.

Formally, an autoencoder consists of two functions, a vector-valued encoder \(g : \mathbb{R}^d \rightarrow \mathbb{R}^k\) that deterministically maps the data to the representation space \(a \in \mathbb{R}^k\), and a decoder \(h : \mathbb{R}^k \rightarrow \mathbb{R}^d\) that maps the representation space back into the original data space.

Learn about most common types of autoencoders and their applications in machine learning. Autoencoders have emerged as one of the technologies and techniques that enable computer systems to solve data compression problems more efficiently. They became a popular solution for reducing noisy data.

Feb 18, 2025 · In this comprehensive tutorial, we will delve into the world of unsupervised learning with autoencoders, focusing on anomaly detection. This powerful technique allows us to identify patterns and outliers in data that may not be immediately apparent through traditional supervised learning methods.

Feb 24, 2024 · Autoencoders are a specific type of feedforward neural networks where the input is the same as the output. They compress the input into a lower-dimensional latent representation and then...