DropBlock vs. Dropout for Regularizing CNNs

Dropout, while effective for fully connected layers, falls short in convolutional layers because of the spatial correlation between nearby pixels. DropBlock addresses this by dropping contiguous blocks of pixels rather than individual ones, thus improving model robustness. On ImageNet data, DropBlock outperforms Dropout with a notable accuracy gain. It is easily implementable in PyTorch with adjustable parameters like block size and drop rate.