Implementation details for iNaturalist 2018 For iNaturalist 2018, following most of the existing work, we use ResNet-50 [10] as backbone network. The **data** augmentation is similar to that used in long-tailed CIFAR datasets except that random cropping with size 224 × 224 is used. To fit two NVIDIA 2080Ti GPUs, we use a batch size of 100 for both SC and PSC based hybrid networks. The networks are trained for 100 epochs using SGD with momentum 0.9 and weight decay 1 × 10 −4 . The initial learning rate is 0.05, which is decayed by a factor of 10 at epoch 60 and epoch 80. Motivated by the fact that iNaturalist has a large number of classes which can make classifier learning more difficult, we assign higher weighting to the classifier learning branch by using a linearly decayed weighting factor α, i.e., α = 1 − T /T max . The temperature τ is set to be 0.1 for both SC and PSC loss functions. For SC loss function, the number of positive samples for each anchor is fixed to 2.