Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The paper uses synthetic with varying brightness and visualizes how Convent uses color.
strengths_and_weaknesses: The paper is concise and reads clearly. The investigation is necessary and an important one. The experiment setup introduced in this paper is a good starting point and can be used as a baseline for further investigations. The experiment could be extended to standard datasets.

The processing of images (feature processing) can affect ConvNet’s ability to identify cue from colors, adding a section on how exactly images were processed would make this reproducible. 

I am not an expert in the field, is there a reason why the color identifying shape was a circle and the noise were squares? Also, I’d think if other variables are kept constant/randomized, ConvNets would identify color (brightness) as the “cue”. However, in any real world setting, shape and edges and color features will always be present  together in dataset. A natural extension of this experiment could be to concatenate color based features separately to improve ConvNets ability to take color as “cue”
suggested_changes: Change dataset where shape, edges and color are interacting together and measure ConvNets ability to use color to distinguish between similar images. Example : green grapes vs black grapes. Water wave forms vs sand wave forms etc.
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The paper investigates which information convolutional neural networks pay attention to in a generic image classfication task. Some experiments are designed and results are presented. The results of intermediate layers of the neural networks are shown to back the claim that CNNs look at color/brightness of pixels along with edge/shape of objects.
strengths_and_weaknesses: Strengths:
1. The topic discussed in the paper is interesting and the paper has a good flow.

Weaknesses:
1. "For simplicity, we focus on how ConvNets understand brightness, rather than 3-channel color." This seems like an alarming deviation from the title: "How Do ConvNets Understand Color?"
2. Following 1, brightness and color seem to be used interchangeably
3. The experiments seem to be designed such that the brightness is the defining factor of discrimination. It seems natural that the neural network picked up on that.
suggested_changes: 1. "... with a base rate performance of 35.3%". How is this calculated? Random assignment of classes?
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper investigates situations where the ConvNet needs to rely on color rather than shape.
strengths_and_weaknesses: Strengths:
i) Various experiments have been conducted.
ii) This work is a first attempt for understanding how ConvNets process image data where color is more important.

Weaknesses:
i) The authors have not exploited a large network.
ii) References in text.
suggested_changes: Please correct the way references are shown in text. Use \citep.
review_title: null

Decision: Invite to archive