Paper: Efficient ConvBN Blocks for Transfer Learning and Beyond (ICLR-2024)

Reviewer comment (Novelty):
the proposed Tune mode looks like an engineering technique. (this method might be more suitable for patent applications)

Author response:
Indeed, we can file patent applications for the method. However, we decide to contribute the method directly to the community, so it can benefit the community without legal constraints.

Reviewer comment (Reproducibility):
As SyncBN's eval mode has similar behavior to BN, I am not sure whether the Tune mode can be directly applied to the backbone trained with SyncBN. The author can explain this in the response.

Author response:
# Whether the Tune mode can be directly applied to the backbone trained with SyncBN

That's a nice catch! SyncBN's Eval mode has the same behavior as BN's Eval mode, so our method can be directly applied to the backbone trained with SyncBN.

Reviewer comment (Novelty):
The method proposed in this article is only applicable to methods that originally used Eval mode, its application scenarios are not very broad, and the impact is relatively small.

Author response:
Thank you for your appreciation. Here are our responses to your concerns:

# Scope and impact

Our work focuses on basic building blocks (ConvBN blocks) in computer vision. As shown in Figure 1 of the paper, training ConvBN blocks with Eval mode takes up a substantial proportion (78.2%) of object detection models, underscoring our work's widespread applicability and impact especially in computer vision and object detection.