A Generic Visualization Approach for Convolutional Neural Networks

An overview of the L2-CAF visualization approach.
L2-CAF mathematical formula. Using gradient descent, optimize the filter f that minimizes the constrained objective L.
The class-specific L2-CAF formula. The constraint objective L maximizes the logit for a particular class while minimizing the logits for all other classes.
Qualitative evaluation for alternative attention constraints. A softmax constraint offers a sparse attention map, while a Gaussian constraint assumes a single-mode (object). The L2-Norm constraint supports multi-mode (objects).
L2-CAF converging in slow motion. L2-CAF supports both classification and feature embedding networks. It can visualize multiple distinct objects with a single input image.
  • [S1] The paper is well-written and the code is released.
  • [S2] L2-CAF enables attention visualization for more complex inputs like 3D images and videos; these areas are rarely explored in visualization literature that focuses mostly on ImageNet and CUB-200.
  • [W1] The paper focuses on quantitative evaluation and provides limited qualitative evaluation. This is one of the weaknesses of the paper because it is a visualization paper and qualitative evaluation can highlight the corner cases of L2-CAF.
  • [W2] Both L2-CAF and CAM reports qualitative, but no quantitative, evaluation for videos. I am not sure, but there is probably a video dataset with object localization that can be used for quantitative evaluation.
  • [W3] L2-CAF is an iterative approach because it uses gradient descent. Thus, Grad-CAM is 7 times faster than L2-CAF on GoogLeNet. Yet, L2-CAF takes 0.3 seconds on GoogLeNet.




I write reviews on computer vision papers. Writing tips are welcomed.

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

How to deliver on Machine Learning projects

10 Best Machine Learning Software | Machine Learning Framework- 2018

Predicting Cuisine Regionality Based on Recipe Ingredients using NLP

Advanced Tensorflow — A complete guide

Conditional Similarity Networks— CSN

What Does it Take to Land a Rocket? Part 2 — The Challenge is Bigger than I Thought

Underfitting and Overfitting in Machine Learning

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Ahmed Taha

Ahmed Taha

I write reviews on computer vision papers. Writing tips are welcomed.

More from Medium

An Introduction to Convolutional Neural Network (CNN)

Understanding Transfer Learning: Unlocking the power of pre-trained models

What’s a neural network in deep learning?

Do We Really Need Backpropagation To Train a Neural Network?