/ConvNets-Visualizer-PyTorch

Implementations of various CNN visualization Techniques in PyTorch

Primary LanguageJupyter Notebook

ConvNets Visualizer in PyTorch

Implementations of various CNN Visualization Techniques in PyTorch. These notebooks are implemented in Google Colaboratory.

Requirements:

You will need a Google Colaboratory Account, all rest dependencies are satisfied within the notebook itself. Note : Select Runtime Environment as Python3 and Hardware as GPU in Colaboratory

Implemented:

  • Deep Dream
  • Layer Activations
  • Per Filter Activations
  • Weight/Feature Visualization
  • Occlusion
  • Saliency : Vanilla Backprop
  • Saliency : Guided Backprop
  • Smooth Grad
  • Neural Texture Synthesis
  • Neural Style Transfer
  • Semantic Dictionaries
  • GradCam
  • Gradient Ascent

Deep Dream (Wait for gif to load)

Total Activations of Each Layer

Per Filter Activations of a Selected Layer

Filters of a Selected Layer(Conv1)

HeatMap by Occlusion

Saliency by Vanilla Backprop

Saliency by Guided Backprop

Saliency by Smooth Grad