xinglab-ai/mda
Revealing Hidden Patterns in Deep Neural Network Feature Space Continuum via Manifold Learning (Nature Communications, 2023)
When you're working with deep learning models, understanding how the model 'sees' and processes information at different internal steps can be difficult. This project helps visualize those hidden patterns by taking the complex, high-dimensional data from inside a deep neural network and transforming it into a clear, lower-dimensional map. This allows researchers and practitioners in fields like medical imaging, genomics, and risk prediction to see how their models are learning and making decisions.
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Use this if you need to interpret the internal workings of a deep neural network and understand how features evolve through different layers for tasks like image segmentation, super-resolution, or predictive modeling.
Not ideal if you are looking for a tool to build or train deep learning models, as this focuses specifically on analyzing pre-trained model features.
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Dec 22, 2023
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