SparK applies masked autoencoder principles to convolutional networks, enabling BERT-style self-supervised pretraining on ResNet and similar architectures. The method uses sparse convolution to handle irregular masked regions and hierarchical reconstruction for multi-scale feature learning. It achieves state-of-the-art results on ImageNet classification and transfers to object detection and instance segmentation tasks.