Title: SVMax: A Feature Embedding Regularizer. Abstract: A neural network regularizer (eg, weight decay) boosts performance by explicitly penalizing the complexity of a network. In this paper, we penalize inferior network activations -- feature embeddings -- which in turn regularize the network's weights implicitly. We propose singular value maximization (SVMax) to learn a uniform feature embedding. The SVMax regularizer integrates seamlessly with both supervised and unsupervised learning. During training, our formulation mitigates model collapse and enables larger learning rates. Thus, our formulation converges in fewer epochs, which reduces the training computational cost. We evaluate the SVMax regularizer using both retrieval and generative adversarial networks. We leverage a synthetic mixture of Gaussians dataset to evaluate SVMax in an unsupervised setting. For retrieval networks, SVMax achieves significant improvement margins across various ranking losses.