We propose a simple but effective data-driven channel pruning algorithm, which compresses deep neural networks effectively by exploiting the characteristics of operations in a differentiable way. The proposed approach makes a joint consideration of batch normalization (BN) and rectified linear unit (ReLU) for channel pruning; it estimates how likely each feature map is to be deactivated by the two successive operations and prunes the channels that have high probabilities. To this end, we learn d…