Generative modeling

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

Generative modeling is the machine-learning task of modeling a data-generating process so that its outputs can be replicated or newly generated.

Generative modeling models a data-generating process so an algorithm can replicate or newly generate its output. A practical image example evolves polygons until their rendered composition resembles a target image. section 3.5

A simple evolutionary implementation turns the idea into an executable pipeline:

• Encode each drawing polygon as a structured gene: three points, color values, and transparency. section 3.5

EvoLisa

EvoLisa evolves a sequence of polygon genes to approximate the Mona Lisa. The experiment demonstrates that generative modeling can replicate an existing visual process, although strong results may require almost a million generations. Alternative rectangle and polyfill brushes invite further experimentation. section 3.5

Figure 3.10 Example of EvoLisa output from Roger Johansson’s blog (https://rogerjohansson.blog/)

Compare the target-like EvoLisa output with the evolutionary image-replication goal. section 3.5

How Evolutionary Deep Learning: Genetic algorithms and neural networks covers it

How deep it goes

Implements encoding, rendering, fitness evaluation, and genetic search, but stops short of developing GAN internals.

Examples

Uses EvoLisa, polygon brushes, target and random renderings, pixel-error fitness, and alternative rectangle or polyfill formats.

What it assumes

Assumes familiarity with deep learning while introducing evolutionary-computation basics before combining the approaches.

What you can do after

Build it from scratch as a DEAP genetic-algorithm pipeline that encodes, renders, evaluates, and evolves image-producing individuals.

How it connects

Connects generative modeling with genetic algorithms, evolutionary computation, generative deep learning, and generative adversarial networks.

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This explainer was generated by Manning's knowledge graph from the text of Evolutionary Deep Learning: Genetic algorithms and neural networks. The section links point to the parts of the book it draws on.