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deep-margins

Excuse me while I organize all the scripts...

A collection of scripts for investigating decision margins in neural networks.

  • input_pipe.py We read in binary data (cat and dog images) using the new Tensorflow Dataset api
  • crop.py Helper script to crop images. Going to be included in input_pipe.py eventually
  • closest_pair.py A script to find the pair of images with the least euclidean distance seperating them. However, this script is inefficient with large (5k+) datasets, and needs to be rewritten using CUDA
  • conv_net.py Simple convolutional network model and training for testing.
  • lin_reg.py For testing.
  • image_generator.py A script to generate a series of images within an n-sphere around an existing image. We use this in an attempt to artifically modify the decision margin of our network.

Distance Measures:https://bib.dbvis.de/uploadedFiles/155.pdf

When is Nearest Neighbor Meaningful?:https://members.loria.fr/MOBerger/Enseignement/Master2/Exposes/beyer.pdf

"...that under certain reasonable assumptions on the data distribution, the ratio of the distances of the nearest and farthest neighbors to a given target in high dimensional space is almost 1 for a wide variety of data distributions and distance functions."

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Investigating margins in deep neural nets. Computing margins and attempting to expand them, thereby increasing "generalization" power.

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