We thank all the reviewers for their constructive comments. We will provide details in the final draft. MCUNet shows consistent improvement across different devices (F746, H743) and tasks (classification, detection). R1: Whether the overall network topology brings major improvement. R2: Why the auto-tuning in TVM fails to work on MCUs.

Existing deep learning based solutions typically restore the target layers individually, or with some concerns at the end of the output, barely taking into account the interaction across thetwostreams/branches. Inorder toutilize information more efficiently, this work presents a general yet simple interactive strategy, namely your trash is my treasure(YTMT), for constructing dual-stream decomposition networks.

Inothercolumns, werandomly masksome tokens (first row), and we sample the invisible tokens based on the visible tokens for the second stage. Here, we show top-1 results in 1 step (second row), and random results in 8 steps (third row),respectively. Interestingly, our model with 95% masked tokens (i.e., 12 tokens are visible among 256 tokens in each channel) is able to generate pluralistic images in only one step by selecting the top 1 token. More importantly, the corresponding results reflect identity attributes of original unmaskedinputs. When the tokens are totally masked (i.e., 100% mask ratio), the model generates plausible and diversity results byrandomly sampling tokens inmultiple steps.