Jul 12, 2020
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 differentiable masks for individual channels and make soft decisions throughout the optimization procedure, which allows to explore larger search space and train more stable networks. The proposed formulation combined with the training framework enables us to identify compressed models even without a separate procedure of fine-tuning. We perform extensive experiments and achieve outstanding performance in terms of the accuracy of output networks given the same amount of resources when compared with the state-of-the-art methods.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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