Towards Clip-Free Quantized Super-Resolution Networks: How to Tame
  Representative Images
Alperen Kalay Bahri Batuhan Bilecen Mustafa Ayazoglu
Super-resolution (SR) networks have been investigated for a while, with their
mobile and lightweight versions gaining noticeable popularity recently.
Quantization, the procedure of decreasing the precision of network parameters
(mostly FP32 to INT8), is also utilized in SR networks for establishing mobile
compatibility. This study focuses on a very important but mostly overlooked
post-training quantization (PTQ) step: representative dataset (RD), which
adjusts the quantization range for PTQ. We propose a novel pipeline (clip-free
quantization pipeline, CFQP) backed up with extensive experimental
justifications to cleverly augment RD images by only using outputs of the FP32
model. Using the proposed pipeline for RD, we can successfully eliminate
unwanted clipped activation layers, which nearly all mobile SR methods utilize
to make the model more robust to PTQ in return for a large overhead in runtime.
Removing clipped activations with our method significantly benefits overall
increased stability, decreased inference runtime up to 54% on some SR models,
better visual quality results compared to INT8 clipped models - and outperforms
even some FP32 non-quantized models, both in runtime and visual quality,
without the need for retraining with clipped activation.