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deepdream of
          a sidewalk

Color

I'm interested in how people experience color. These pages are a work in progress. As I write this, I'm hoping to try and begin to form an answer to the following 2 questions:

  • In the space that the human brain builds to represent the surrounding world, the color that the brain assigns to a surface of an object in this space is dependent on many things in addition to the light coming from the corresponding surface in the real world. This is a known phenomenon, and one of the reasons people developed the field of color appearance models. With this in mind, it is interesting to ask: do modern neural networks for image recognition integrate the same information to identify certain colors? If not, there are likely conditions under which they perform poorly as a result.

    The above question can be played in reverse: if neural networks do model colors well, then will inspecting the internals of a trained network help reveal how color might be processed in human brains?

Experiments below are exported from Jupyter Lab notebooks from this project's git repository.

Concept

This page is a scrapbook of ideas for the project. Color and neural networks The most general question I want to ask is: What can be said about the relationship between neural networks for vision and human color perception? In many ways, this question has issues. The topic of human color perception is the subject of deep unanswered questions. Color is a property of our mind, an appearence in consciousness and I am currently of the belief that we won’t get a satisfying answer to the question what is color? Read more...

Experiment 1

Creating an orange vs. brown dataset.

Experiment 1.2

Looking for ImageNet classes that differ only in color.

Experiment 1.3

Color dot dataset.

Experiment 1.3.1

ResNet transfer learning for the orange-brown-neither dataset.

Experiment 1.3.2

Repeat 1.3.1 (fixing a mistake of 1.3.1)

Experiment 1.4

Testing orange and brown at the final pool layer.

Experiment 1. Summary.

A high-level overview of progress in experiment 1.

Experiment 2

Searching for a image classification fail case by varying illumination.

Experiment 2.0.1

Searching for an image classification fail case by varying illumination (2nd attempt).