1, All the experiments are conducted using images under 224*224 resolution, it would be interesting to see how the performance will be if we use a larger resolution. 2. The accuracy with lower resolutions for some examples is even better than the model with full resolution. Is there any underlying reason for this phenomenon? 3, It seems the improvement over the flops does align well with that over the real latency as shown in fig.3 and tab.3. It would be good to provide the performance and speed trade-off for real acceleration. 4, For the training process, the base models will be first trained and then combined with the resolution selector network for fine-tuning. I’m wondering if it is possible to train the whole model from scratch?
Some minor issues: Line121: “The first is the large classifier network with both high performance and expensive computational costs is first trained”, is the “is first trained” redundant?
1, All the experiments are conducted using images under 224*224 resolution, it would be interesting to see how the performance will be if we use a larger resolution.