Image restoration, aiming to restore high-quality images from their degraded counterparts, is a fundamental computer vision problem and has been studied for many years. Due to its ill-posed nature, early research efforts [1,2,3]typically focus on developing single-task models, with each model handling only one specific degradation.

In this work, we demonstrate that these two seemingly orthogonal concepts are remarkably well-suited for each other.

We explore combining dropout with robust training methods and obtain better generalization.

Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks. Recently, graph neural networks (GNNs) have been successfully applied to graph-structureddata such as point cloud and molecular data. These networks often only consider pairwise dependencies, as they operate on a graph structure.

A.1 Descriptions of backdoor attack algorithms In addition to the basic information in Table 1 of the main manuscript, here we describe the general idea of eight implemented backdoor attack algorithms in BackdoorBench, as follows. A.2 Descriptions of backdoor defense algorithms In addition to the basic information in Table 2 of the main manuscript, here we describe the general idea of nine implemented backdoor defense algorithms in BackdoorBench, as follows. It is used to determine the number of pruned neurons. Running environments Our evaluations are conducted on GPU servers with 2 Intel(R) Xeon(R) Platinum 8170 CPU @ 2.10GHz, RTX3090 GPU (32GB) and 320 GB RAM (2666MHz). With these hyper-3 Table 2: Hyper-parameter settings of all implemented defense methods.