In seeking for sparse and efficient neural network models, many previous
works investigated on enforcing L1 or L0 regularizers to encourage weight
sparsity during training. The L0 regularizer measures the parameter sparsity
directly and is invariant to the scaling of parameter values, but it cannot
provide useful gradients, and therefore requires complex optimization
techniques. The L1 regularizer is almost everywhere differentiable and can be
easily optimized with gradient descent. Yet it is not scale-invariant, causing
the same shrinking rate to all parameters, which is inefficient in increasing
sparsity. Inspired by the Hoyer measure (the ratio between L1 and L2 norms)
used in traditional compressed sensing problems, we present DeepHoyer, a set of
sparsity-inducing regularizers that are both differentiable almost everywhere
and scale-invariant. Our experiments show that enforcing DeepHoyer regularizers
can produce even sparser neural network models than previous works, under the
same accuracy level. We also show that DeepHoyer can be applied to both
element-wise and structural pruning.

本论文提出了一组可微且尺度不变的模型压缩正则化项 DeepHoyer，通过实验表明在相同的精度水平下，使用 DeepHoyer 压缩神经网络可以得到更加稀疏的模型，可以运用于元素级和结构级剪枝。