Intermediate · Research
Denoising
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A self-supervised learning technique where a model learns to remove artificial noise added to data.
Technical Definition
A self-supervised learning technique where a model learns to remove artificial noise added to data.
How it works
Denoising is a self-supervised learning approach where noise is intentionally added to input data, and the model's task is to reconstruct the original, clean data. This process forces the model to learn underlying patterns and representations without explicit labels. It's commonly used in natural language processing and image processing tasks.
Related Concepts
- Self-Supervised Learning — A training paradigm that generates supervisory signals from the data itself, eliminating the need for human labels.
- Unsupervised Learning — Learning patterns from data that has no labels — only the inputs.
- Representation Learning — The process of automatically discovering meaningful representations of data from raw inputs.