Comments (3)
Thanks for your great paper on SR quantization. I have one problem about the method:
DAIA, Is there any other difference from LSQ expcept your first warm-up to initilize the step size?
or did you make specification of LSQ for SR task, thus you get your Distribution-Aware Interval Adaptation?
Another problem, Is there experiment results which compare with LSQ or EWGS?
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Hi, we followed the LSQ as the quantizer in the SR task. The main contributions in my opinion are:
- Quantizing all layer: not only the non-linear mapping, but also the extraction and reconstruction.
- The self-supervised loss function, which show obvious benefit on SR task.
For comparasion with LSQ, please refer to Table 5. There is about 1.3 PSNR improvement of FQSR over the LSQ.
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Re-open if further questions
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Related Issues (9)
- Biases and BatchNorm not quantized as described in "AQD: Towards Accurate Quantized Object Detection" HOT 2
- ADQ HOT 4
- AQD HOT 1
- loss become infinite while training quant models HOT 1
- The sper
- EDSR-PyTorch can not be found HOT 2
- NameError: name 'task_cls' is not defined HOT 1
- https://github.com/blueardour/detectron2 404 HOT 2
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