Efficient Equivariant Network Supplementary Materials AMNIST-rot Model Architecture

Please refer to Table 5. Table 5: Architecture of E4-Net on Mnist-rot classification, p means dropout rate. The hyperparameters we use in this architecture are kernel size k = 5, reduction ratio r = 1, and the number of slices s = 2. In the large model, we increase the channel dimension to 24, the number of slices to 12, the reduction ratio to 2, and keep other hyperparameters the same. We take ResNet-18 [2], which is composed of an initial convolution layer, followed by 4 stage Res-Blocks and one final classification layer.