Reconstruction loss

As explained in Delivering Machine Learning Projects: From design to deployment

A reconstruction error is the discrepancy between an input and the output reconstructed from its encoded representation.

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes

Defines reconstruction error as input-output discrepancy and applies it operationally, without detailing the loss formula or autoencoder internals.

Examples

Measures reconstruction errors for individual news articles against an autoencoder trained on regional historical articles.

What it assumes

Assumes an autoencoder trained on historical articles and an anomaly-detection workflow.

What you can do after

Diagnose anomalous articles by comparing their reconstructions with their inputs.

How it connects

Connects autoencoders with anomaly detection and historical data.

liveBookmanning.com

Read Delivering Machine Learning Projects: From design to deployment on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Outlier Detection in PythonReconstruction error is a measure of the discrepancy between an observation and the output reconstructed from its encoded or generated representation.
• 

  liveBookmanning.com
  Math and Architectures of Deep LearningThe discrepancy between an input and its reconstruction.
• 

  liveBookmanning.com
  Graph Neural Networks in ActionA measure of the discrepancy between an input and the output reconstructed from its latent representation.
• 

  liveBookmanning.com
  Build a Text-to-Image Generator (from Scratch)A reconstruction loss measures the discrepancy between input data and the data reconstructed from its latent representation.
• 

  liveBookmanning.com
  Learn Generative AI with PyTorchThe loss term that measures pixel-level mismatch between an input image and its reconstruction in a VAE.
• 

  liveBookmanning.com
  Financial AI in PracticeA reconstruction error is the difference between an input and the output produced when a model attempts to reconstruct that input.
• 

  liveBookmanning.com
  Deep Learning with R, Third EditionA reconstruction loss measures the discrepancy between original data or representations and their model-generated reconstructions.
• 

  liveBookmanning.com
  Evolutionary Deep Learning: Genetic algorithms and neural networksReconstruction loss is a loss value measuring the discrepancy between an input example and the output reconstructed from it.
• 

  liveBookmanning.com
  Deep Learning with Python, Third EditionA loss that measures how well a model reproduces its input or a corrupted version of it.

Compare how 10 books explain reconstruction loss →

Related concepts

• variational autoencoderused by · A variational autoencoder is a generative neural network that learns a probabilistic latent representation of data and decodes samples from it to reconstruct or generate data.
• mean squared errormeasured by · Mean squared error is the arithmetic mean of the squared differences between corresponding predicted and target numerical values.
• mean absolute errormeasured by · Mean absolute error is the arithmetic mean of the absolute differences between corresponding predicted and reference numerical values.
• anomaly scoreequivalent to · An anomaly score is a numerical measure of how anomalous an observation or event is relative to a reference or expected pattern.
• autoencoderproduced by · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.
• principal component analysisused by · Principal component analysis is a linear dimensionality-reduction technique that transforms data into orthogonal components ordered by the variance they capture.

This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.