This repository contains code to run experiments in the paper "Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers." There are three subdirectories in this repository, the contents of which are described below. This code was tested using PyTorch 1.7.

This part of the repository is for running the synthetic pairs matrix experiments in the paper. Here are the commands to run all of the experiments in the paper:

Pairs Matrix 1

python main.py --exp_name pairs_matrix1 --pattern_dir pairs_matrix1 --imgnet_augment

Pairs Matrix 2

python main.py --exp_name pairs_matrix2 --pattern_dir pairs_matrix2 --imgnet_augment

Color Deviation

python main.py --exp_name color_deviation_(your epsilon here) --pattern_dir pairs_matrix1 --hue_perturb blue_circle --hue_perturb_val (your epsilon here) --imgnet_augment

Color Overlap (pattern dirs are already predefined for these. Some overlap values are included, but if you would like to use different ones, you must create them yourself.)

python main.py --exp_name color_overlap_(your overlap here) --pattern_dir color_overlap_(your overlap here) --imgnet_augment

Predictivity

python3 main.py --exp_name predictivity_(your predictivity here) --pattern_dir pairs_matrix1 --pred_drop blue --pred_drop_val (your predictivity here)

When you run one of these experiments, datasets will be created and models trained. Datasets will get created and stored in the directory ./data/exp_name, trained models will get stored in ./models/exp_name, and results will appear in ./results/exp_name. When the experiment is done, there should be a file called master.csv in the directory ./results/exp_name which will contain information including each feature's average preference over the course of the experiment, pixel count, and name. A complete list of commands to generate all data in the paper can be found in the commands.sh file in the pairs_matrix_experiments subdirectory. The training script is adapted from the torchvision training script: https://github.com/pytorch/examples/blob/master/imagenet/main.py.

Stimuli and helper code is used from the open-sourced code of the paper "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness" (https://github.com/rgeirhos/texture-vs-shape).

To run the experiments from our paper with an ImageNet-trained ResNet-50, you can do the following:

Normal Texture Bias

python main.py

Varying degrees of background interpolation to white (use 0 for completely white, 1 for texture background).

python main.py --bg_interp (your interpolation here)

Resizing

python main.py --bg_interp 0 --size (your fraction of the object size here)

Landscapes

python main.py --bg_interp 0 --landscape

Only full shapes

python main.py --only_complete

Only full shapes masked with masked/interpolated background

python main.py --only_complete --bg_interp (your interpolation here)

A complete list of commands to generate all of the texture bias data from our paper can be found in the commands.sh file in the texture_bias subdirectory.

Running these experiments is a bit more involved. A complete list of commands you must run to reproduce all data and graphs found in the paper can be found in the commands.sh file in the excessive_invariance subdirectory. Comments in the file describe what each step represents.