dlsia
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Getting Started:

  • Welcome to dlsia’s documentation!
  • Install dlsia
  • Network initialization
  • Training
  • Saving and loading models

Basic dlsia Workflows

  • Segmention demo in 2D using Mixed-scale Dense Networks and Tunable U-Nets
  • Demo on how to save and load models

SMSNet Ensemble Learning

  • Ensemble Learning with Randomized Sparse Mixed-Scale Networks

Image Denoising

  • Supervised Image Denosing with MSDNets and SMSNet ensembles
  • Self-supervised denoising using ensembles

Image Classification

  • Image classification via an ensemble of Randomized Sparse Mixed Scale Networks
  • Image classification with Randomized Sparse Mixed Scale Autoencoders, regularized by the availability of image labels

Autoencoder Latent Space:

  • Latent Space Exploration with UMap and Randomized Sparse Mixed Scale Autoencoders
dlsia
  • Python Module Index

Python Module Index

d
 
d
- dlsia
    dlsia.core
    dlsia.core.conformalize
    dlsia.core.conformalize.conformalize_segmentation
    dlsia.core.corcoef
    dlsia.core.custom_losses
    dlsia.core.helpers
    dlsia.core.inference_scripts
    dlsia.core.networks
    dlsia.core.networks.aggnet
    dlsia.core.networks.baggins
    dlsia.core.networks.graph_utils
    dlsia.core.networks.msd_graph_tools
    dlsia.core.networks.msdnet
    dlsia.core.networks.scale_up_down
    dlsia.core.networks.smsnet
    dlsia.core.networks.sparsenet
    dlsia.core.networks.tunet
    dlsia.core.networks.tunet3plus
    dlsia.core.train_scripts
    dlsia.test_data
    dlsia.test_data.two_d
    dlsia.test_data.two_d.build_test_data
    dlsia.test_data.two_d.diffusion_model
    dlsia.test_data.two_d.noisy_gauss_2d
    dlsia.test_data.two_d.noisy_gauss_2d_time
    dlsia.test_data.two_d.random_shapes
    dlsia.test_data.two_d.torch_hdf5_loader
    dlsia.test_data.two_d.tst
    dlsia.viz_tools
    dlsia.viz_tools.plots

© Copyright 2023, Peter Zwart & Eric Roberts. Revision 458d70d0.

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