Reply to Reviewer THqa
Thank you again for your time and feedbacks!
#### **IRB: Given human subjects, the authors should try to obtain IRB, even if it is extensive (for a reason).**
We thank the reviewer for mentioning. We have initiated IRB and will include in our final version of the paper.
#### **Jitters: Can the authors provide some visualizations/insights into how the model is doing that?**
During training, we apply augmentations (jitters, false strokes, distortions etc.) to the sketch and ask the model to reconstruct the original sketch, thereby forcing the model to learn to remove noise and distortions. In Figure 6 (main paper) we show ablations on models trained with different augmentations. As can be seen from the 1st, 2nd and 4th image where jitters exist on the top edges of these images, models trained with structural and false-stroke augmentations are not able to remove the jitter, whereas the model trained with Local (e.g. jitter) augmentation successfully removes the jitter.
In addition, we run additional inferences on jittered sketches. In the resulting generations, all the jitters are removed as expected. We have sent the anonymous sharing link to AC per policy, who shall in turn release it.
#### **Human-like augmentations: Can the authors clarify why are the Atoms of Recognition and Extreme Image Transformations not useful here for robustness or generalization?**
Due to the length limit we were not able to expand on this topic in the above reply. Below is a more detailed discussion:
Atoms of Recognition [a] discovers the phenomenon where humans fail sharply to recognize objects with a tiny degradation from Minimal Recognizable Configurations(MIRC), which is not observed on computational models. Instead of improving robustness/generalizability of computational models, this work mainly focuses on the pitfall of human recognition instead of machine’s, which seems to be tangential to our focus. However, it could be interesting to conduct sketch-based MIRC for machines and see if any improvement can be derived, which we will leave for future work.
Extreme Image Transformations (EIT) [b] utilizes global transformations (e.g. grid/full shuffle, color flatten etc.) to improve classification/detection robustness and generalizability by forcing the model to learn global representation. In our case, given black and white sketches, only grid shuffle is applicable, and we indeed considered grid shuffle (Jigsaw) as one of the augmentation initially, but the resulting sketch often became incomprehensible even to human eyes – because of the high versatility and the absence of color and textual information in sketches.
In terms of “Human-like augmentations”, the augmentations we implemented are intended to simulate “human-like” errors made during air-drawing, while both Atoms of Recognition and EITs are not “Human-like” augmentations.
#### **Negative societal impact: There could be an adverse adoption of this work for generating mockery/meme videos, specially in the teen category. What do the authors think?**
We thank the reviewer for raising the point. In general generative AI can result in negative impact if misused, and we agree in our case the negative impact can be specifically to teenagers. We will add it to the final version of the paper.
[a] Atoms of recognition in human and computer vision
[b] Extreme image transformations affect humans and machines differently