Multi-scale neural networks have shown effectiveness in image restoration tasks, which are usually designed and integrated in a handcrafted manner. Different from the existing labor-intensive handcrafted architecture design paradigms, we present a novel method, termed as multi-sCaLe nEural ARchitecture sEarch for image Restoration (CLEARER), which is a specifically designed neural architecture search (NAS) for image restoration.

In the paper, we present a multi-scale search space which is casted into a differentiable supernet consisting of three modules, i.e., parallel module, transition module, and fusion module. As shown in Figure 1.(a), there are As mentioned in the main body of the paper, the super-network we build for restoration contains three cells and each cell consists of four cascade modules. Namely, there are 12 cascade modules in total. The strided convolution is used to down sample features. The convolutional sequence is arranged in a residual manner for each parallel direction.

Weaknesses: 1: Limited novelty: CLEARER uses multi-scale search space that consists of three types of modules: parallel module, transition module, and fusion module. All of these modules were originally proposed in [2, 1].The authors did not cite these works when mentioning the said modules throughout the paper. It seems inconvenient, as for every new task we would have a different architecture. However, they did not provide any analysis/insights of what makes it specific for image restoration. For instance, what makes it suitable for image denoising and image deraining, OR why it would not work for any other applications such as semantic segmentation?

The paper initially received mixed ratings, but the reviewers were convinced by the rebuttal. Overall, their assessment is positive; the area chair agrees with their assessment and recommends an accept.

Multi-scale neural networks have shown effectiveness in image restoration tasks, which are usually designed and integrated in a handcrafted manner. Different from the existing labor-intensive handcrafted architecture design paradigms, we present a novel method, termed as multi-sCaLe nEural ARchitecture sEarch for image Restoration (CLEARER), which is a specifically designed neural architecture search (NAS) for image restoration. On one hand, we design a multi-scale search space that consists of three task-flexible modules. Namely, 1) Parallel module that connects multi-resolution neural blocks in parallel, while preserving the channels and spatial-resolution in each neural block, 2) Transition module remains the existing multi-resolution features while extending them to a lower resolution, 3) Fusion module integrates multi-resolution features by passing the features of the parallel neural blocks to the current neural blocks. On the other hand, we present novel losses which could 1) balance the tradeoff between the model complexity and performance, which is highly expected to image restoration; and 2) relax the discrete architecture parameters into a continuous distribution which approximates to either 0 or 1. As a result, a differentiable strategy could be employed to search when to fuse or extract multi-resolution features, while the discretization issue faced by the gradient-based NAS could be alleviated.