  A central goal in deep learning is to learn compact representations of
features at every layer of a neural network, which is useful for both
unsupervised representation learning and structured network pruning. While
there is a growing body of work in structured pruning, current state-of-the-art
methods suffer from two key limitations: (i) instability during training, and
(ii) need for an additional step of fine-tuning, which is resource-intensive.
At the core of these limitations is the lack of a systematic approach that
jointly prunes and refines weights during training in a single stage, and does
not require any fine-tuning upon convergence to achieve state-of-the-art
performance. We present a novel single-stage structured pruning method termed
DiscriminAtive Masking (DAM). The key intuition behind DAM is to
discriminatively prefer some of the neurons to be refined during the training
process, while gradually masking out other neurons. We show that our proposed
DAM approach has remarkably good performance over various applications,
including dimensionality reduction, recommendation system, graph representation
learning, and structured pruning for image classification. We also
theoretically show that the learning objective of DAM is directly related to
minimizing the L0 norm of the masking layer.
