Response to Reviewer Bh7W
We thank Reviewer Bh7W for contributing time reviewing and providing valuable feedback. We are encouraged that the reviewer found our work strongly motivated, easy to follow, and comprehensively experimented with. More importantly, we address the reviewer's concerns and questions below in detail:
**Q1-1: Novelty in channel-wise quantization factor** \
**A1-1:**
As the reviewer also pointed out, there are works on SR quantization that adopt channel-wise quantization functions (e.g., DAQ [A]). However, we would like to note that simply adopting channel-wise scaling and offsets incur substantial computational overhead. Instead, our method adopts only scaling for particular layers or shifting for certain layers and even none for some layers, which effectively reduces the computation overhead (e.g., the bitOPs overhead is $\sim\times 100$ smaller compared to DAQ [A]). We find our *selective* approach for offsets to be helpful for achieving accurate quantization accuracy without incurring significant overhead.
**Q1-2: Channel-wise quantization will not be affected by the value differences between channels** \
**A1-2:**
We would like to clarify that we utilize layer-wise quantization factor for each layer. For a few layers, we shift or scale the input of the convolutional layer with channel-wise offsets, which are then quantized with a layer-wise quantization factor. Thus, the quantization accuracy is affected by value differences in channels.
**Q2: Repeated strategies for one problem** \
**A2:**
As the reviewer mentioned, both variance regularization and selective offset strategy alleviate the distribution mismatch problem in SR. Using two components together gives a relatively minor gain (0.09 dB / 0.18 dB) compared to the gain from each component (0.8 dB / 0.9 dB). This indicates that there is, to some extent, an overlapping effect of the two strategies. Nevertheless, both components reduce the mismatch, resulting in a further accurate quantized SR network. We will add further discussion of the overlap to our supplementary material.
**Q3: More insightful analysis and discoveries** \
**A3:**
Thanks for your suggestion, we added deeper analyses on the distribution mismatch problem in Section S5.1 of the supplementary material: how solving such a problem is especially crucial for SR networks and how our method effectively reduces the mismatch. Also, as suggested by another reviewer, we added analysis on the gradient conflict problem in Section S5.2. Please kindly refer to the supplementary material for more details.
**Q4: Will variance regularization lead to homogenization of features?** \
**A4:**
Directly using variance regularization can lead to the homogenization of features, which can trigger a PSNR loss, as shown in our ablation study. Since we use variance regularization loss cooperatively with the reconstruction loss, the features are not fully homogenized, as an example is visualized in Figure S2 of the supplementary material.
Moreover, we tried regularizing the difference in the mean of each channel, which gave a slight increase in accuracy for a few settings. Thank you for your constructive suggestions, we will integrate such an analysis into our manuscript.
Table R4: **Variance regularization** on EDSR x4 (2-bit).
| Method | Set5 | Set14 | B100 | Urban100 |
|:-------------|:-----:|:-----:|:-----:|:------:|
| Regularize std of feature | 31.49 | 28.12 | 27.26 | 25.15 |
| Regularize std of ch-wise mean | 31.51 | 28.15 | 27.29 | 25.21 |
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[A] DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks, WACV2022.