In this research we proposed the general subspace capsule networks (SCNs) that can be successfully applied in generative as well as discriminative tasks. Subspace capsule networks model the possible variations in the appearance of entities trough a group of learned capsule subspaces. Then the capsules are created by projecting the input feature vector onto these learned capsule subspaces using leaned transformations.
Result: We evaluated the effectiveness and generalizability of SCN through a comprehensive set of experiments in three applications namely, high resolution image generation, semi-supervised image classification using the GAN framework and supervised image classification. We demonstrate the scalability of SCN to large datasets and model architectures by applying it on supervised classification of ImageNet dataset.