Supplementary Material of ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding, Shan You

The CIFAR-10 dataset has 60,000 colored images in 10 classes, with 50,000 images for training and 10,000 images for testing. The images are normalized by mean and standard deviation. As convention, we perform the data augmentation by padding each image 4 pixels filled with 0 on each side and then randomly cropping a 32 32 patch from each image or its horizontal flip. The ImageNet dataset contains 1.2 million training images, 50,000 validation images, and 100,000 test images in 1,000 classes. We adopt the standard data augmentation for training.