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Multi-view learning (mvlearning)

Framework for different fusion strategies in multi-view learning with PyTorch. Academic purposes

Multi-view Learning

Examples

For definitions on the concepts used here please look at Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications.

Usage

  • For Input fusion (with feature concatenation) you can just create it with
from mvlearning.fusion import InputFusion
InputFusion(single_pytorch_model, view_names=["a list of string", "with the names of the views"])
  • For Decision fusion (with averaging output) you can just create it with
from mvlearning.fusion import DecisionFusion
DecisionFusion({"view 1": pytorch_model1, "view 2": pytorch_model2, ...})
  • For Feature fusion you can just create it with
from mvlearning.fusion import FeatureFusion
FeatureFusion({"view 1": pytorch_encoder1, "view 2": pytorch_encoder2, ...}, pytorch_merge_module, pytorch_model_head)

Details

  • The encoders have to had a function called get_output_size where the dimension of the output is returned.
  • For detailed examples see the Examples folder.

Install

  • For installation you can run:
pip install --editable .

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