Distributed Convolutional Neural Networks: Learning Disentangled Feature Embeddings

This paper proposes a loss function for training the distributed convolutional neural network (DisCNN), which embeds positive-class features into a compact ball and drives the embeddings of dissimilar negative-class features toward the origin. We theoretically validate that the convolutional layers of DisCNN exclusively extract positive features while completely ignoring any dissimilar features, thereby fully disentangling positive features from dissimilar features. Furthermore,We verify a positive monotonic association between the presence probability of positive features and the L2-norms of feature embeddings. The model achieves favorable generalization performance on test data, even on training-unseen samples bearing partial positive features. Moreover, a preliminary experiment demonstrates DisCNN’s potential for object detection.