A Experiment Details and Complete Results

A.2 Model Architectures In this section we describe in detail each of the model architectures we use in our experiments. Our small ConvNet consists of the following layers: A convolutional layer with 32 kernels of size 3 3 and ReLU activation. A max pooling layer with pool size 2 2. A flatten layer. For inputs of shape 32 32 3, this model has 21,697 parameters. Our large ConvNet model consists of the following layers: A convolutional layer with 32 kernels of size 3 3, padding, and ReLU activation.