🛰️
SIT-FUSE
  • 🛰️SIT-FUSE Docs
    • Machine Learning Approach
    • 🤖Self-Supervised Representation Learning
      • RBMs and DBNs
      • BYOL
      • I-JEPA
    • 🔲Self-Supervised Segmentation
  • User Guide
    • Installing the repo
    • Code Configuration
      • Training and Dataset Generation
        • Config Options
          • Encoder Config Options
            • Encoder Training Config Options
              • Training data Stratification Config Options
          • DBN-Specific Config Options
          • BYOL-Specific Config Options
          • I-JEPA-Specific Training Options
          • Deep Clustering Config Options
            • Deep Clustering Training Config Options
            • Hierarchical Deep Clustering Config Options
              • Hierarchichal Deep Clustering Training Config Options
          • Logger Config Options
          • Scaler Config Options
          • Output Config Options
          • Data Config Options
            • Reader Options
        • Example Configs
          • Example DBN Config
          • Example BYOL Config
          • Example I-JEPA Config
      • Geolocation Application
        • Config Options
          • Data Config Options
          • Context Config Options
        • Example Config
      • Data combination, colocation, and resampling
        • Config Options
          • Low Res Config Options
            • Data Config Options
          • High Res Config Options
            • Data Config Options
          • Fusion Config Options
        • Example Config
    • Dockerized Environment
    • Conda Virtual Environment
    • Training
      • Training Dataset Preprocessing Examples
      • Training Command Examples
      • Inference Command Examples
      • Training Summary
    • File Conversion
    • Post-processing
      • Labeling Tutorial
        • QGIS configuration
        • Understanding the data
        • Creating and modifying shapefiles
        • Other shapefile tools
      • Zonal Histogram
        • Creating the YAML file
        • Gathering files
        • Code configuration
        • Getting output
      • Context Assignment
        • Class comparison
        • GeoTiff generation
        • Contours
  • SIT FUSE Comparison
    • Precise Output
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  1. SIT-FUSE Docs

Machine Learning Approach

PreviousSIT-FUSE DocsNextSelf-Supervised Representation Learning

Last updated 11 months ago

SIT-FUSE combines self-supervised representation learning with self-supervised segmentation. The below subpages describe what options are available and how each piece works.

🛰️
🤖Self-Supervised Representation Learning
🔲Self-Supervised Segmentation