# Learning by Denoising Part 2. Connection between data distribution and denoising function

In this blog post, we complement our first post and examine denoising from a more analytic perspective with detailed mathematical derivations. We will show that there is a unique two-way connection between the uncorrupted data distribution $$p(x)$$ and the optimal denoising function $$g(\tilde x)$$, provided that the corruption noise is Gaussian. The corrupted distribution $$p(\tilde x)$$ plays a central role (more…)

# Learning by Denoising Part 1: What and why of denoising

Unsupervised learning tasks support the main tasks of supervised training by somehow modeling the input distribution, $$p(x)$$. Denoising is no exception. When we use denoising as an auxiliary task, we are not interested in denoising itself, neither are we interested in taking samples from $$p(x)$$ or computing the probability of the data. What we want is to extract features that describe the data and which are useful for our primary task of supervised learning. (more…)

# Curious AI Blog is here

The Curious AI Company blog kicks off with a series of posts called “Learning by denoising”. In this series, we want to demonstrate why denoising is a good (for certain tasks, perhaps the best) way to do unsupervised learning. (more…)

# The Curious AI Blog

Come hither! Deeper learning topics from the minds behind Ladder Network and Tagger!

This blog is a gentle introduction to the state-of-the-art machine learning technology from The Curious AI Company, culminating in the Ladder and Tagger networks. In other words, read on if you’re interested in unsupervised and semi-supervised classification and perceptual grouping tasks!