# byronliang8/hubnessgansampling architecture

> Architecture diagram of the GitHub repository byronliang8/hubnessgansampling, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-28). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/byronliang8/hubnessgansampling
- Repository: https://github.com/byronliang8/hubnessgansampling

## Overview

This repository is a Colab research artifact demonstrating a GAN latent-sampling method, rather than a general-purpose application. The documented workflow uses hubness priors to identify promising latents before image synthesis; the paper also frames truncation as an approximation to hub latents’ central-clustering effect. The tree contains only one notebook, so the diagram reflects documented behavior rather than inspected implementation details. The README identifies StyleGAN2 as the research basis but does not specify the notebook’s exact model-loading or synthesis calls.

Main entry points: [`Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb`](https://github.com/byronliang8/hubnessgansampling/blob/main/Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb) (Colab Notebook)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_workflow["Notebook Workflow"]
  node_notebook["Colab Notebook"]
  node_images["Synthesized Images"]
end

subgraph group_method["Hubness Method"]
  node_hubness_priors["Hubness Priors"]
  node_latent_selection["Quality Latent Selection"]
  node_truncation["Truncation Comparison"]
end

subgraph group_gan["GAN Synthesis"]
  node_latent_distribution["GAN Latent Distribution"]
  node_stylegan2["StyleGAN2 Synthesis"]
end

node_researcher(("Researcher"))

node_researcher -->|"runs"| node_notebook
node_notebook -->|"explores"| node_hubness_priors
node_latent_distribution -->|"provides latents"| node_hubness_priors
node_hubness_priors -->|"guides selection"| node_latent_selection
node_latent_selection -->|"selects latents"| node_stylegan2
node_stylegan2 -->|"synthesizes"| node_images
node_notebook -->|"compares"| node_truncation

click node_notebook "https://github.com/byronliang8/hubnessgansampling/blob/main/Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb"
click node_hubness_priors "https://github.com/byronliang8/hubnessgansampling/blob/main/Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb"
click node_latent_selection "https://github.com/byronliang8/hubnessgansampling/blob/main/Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb"
click node_truncation "https://github.com/byronliang8/hubnessgansampling/blob/main/Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_notebook,node_images toneBlue
class node_hubness_priors,node_latent_selection,node_truncation toneAmber
class node_latent_distribution,node_stylegan2 toneMint
class node_researcher toneIndigo
```

## Components

### Notebook Workflow

- [Colab Notebook](https://github.com/byronliang8/hubnessgansampling/blob/main/Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb): `Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb`
- **Synthesized Images**

### Hubness Method

- [Hubness Priors](https://github.com/byronliang8/hubnessgansampling/blob/main/Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb): `Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb`
- [Quality Latent Selection](https://github.com/byronliang8/hubnessgansampling/blob/main/Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb): `Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb`
- [Truncation Comparison](https://github.com/byronliang8/hubnessgansampling/blob/main/Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb): `Colab/Exploring_and_Exploiting_Hubness_Priors_for_High_Quality_GAN_Latent_Sampling.ipynb`

### GAN Synthesis

- **GAN Latent Distribution**
- **StyleGAN2 Synthesis**

### Other components

- **Researcher**

## Connections

- Researcher → Colab Notebook: runs
- Colab Notebook → Hubness Priors: explores
- GAN Latent Distribution → Hubness Priors: provides latents
- Hubness Priors → Quality Latent Selection: guides selection
- Quality Latent Selection → StyleGAN2 Synthesis: selects latents
- StyleGAN2 Synthesis → Synthesized Images: synthesizes
- Colab Notebook → Truncation Comparison: compares