  To address the limitations of existing magnitude-based pruning algorithms in
cases where model weights or activations are of large and similar magnitude, we
propose a novel perspective to discover parameter redundancy among channels and
accelerate deep CNNs via channel pruning. Precisely, we argue that channels
revealing similar feature information have functional overlap and that most
channels within each such similarity group can be removed without compromising
model's representational power. After deriving an effective metric for
evaluating channel similarity through probabilistic modeling, we introduce a
pruning algorithm via hierarchical clustering of channels. In particular, the
proposed algorithm does not rely on sparsity training techniques or complex
data-driven optimization and can be directly applied to pre-trained models.
Extensive experiments on benchmark datasets strongly demonstrate the superior
acceleration performance of our approach over prior arts. On ImageNet, our
pruned ResNet-50 with 30% FLOPs reduced outperforms the baseline model.
